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ACROA: Artificial Chemical Reaction Optimization Algorithm for global optimization
Abstract
Heuristic based computational algorithms are densely being used in many different fields due to their advantages. When investigated carefully, chemical reactions possess efficient objects, states, process, and events that can be designed as a computational method en bloc. In this study, a novel computational method, which is more robust and have less parameters than that of used in the literature, is intended to be developed inspiring from types and occurring of chemical reactions. The proposed method is named as Artificial Chemical Reaction Optimization Algorithm, ACROA. Applications to multiple-sequence alignment, data mining, and benchmark functions have been performed so as to put forward the performance of developed computational method.
... To understand it, the work of Alatas has been considered. It talked about the Artificial Chemical Reaction Optimization Algorithm (CRO), a fascinating strategy that builds a reliable computer method by taking cues from the effectiveness of chemical reactions [8]. Objective of this study is to reduce the number of parameters and boost the robustness of a computational algorithm by utilizing the natural events and processes of chemical reactions. ...
... Furthermore, the Adaptive Neuro-Fuzzy Inference System (ANFIS) technique, as discussed by Rajkumar et al. [7], has shown promise in optimizing cost and efficiency. The Chemical Reaction Optimization (CRO) algorithm, inspired by natural processes, offers a novel approach to solving optimization problems, as detailed in studies Case Study by Alatas [8] and Siddique et al. [9]. As Nayak et al. [11] proposed, combining CRO with machine learning could enhance predictive accuracy and optimization efficiency. ...
... As Nayak et al. [11] proposed, combining CRO with machine learning could enhance predictive accuracy and optimization efficiency. The application of the CRO algorithm, as discussed by Alatas [8], Siddique et al. [9], and Luo et al. [10], introduces an innovative approach to optimization. CRO's ability to handle complex problems with fewer parameters and its adaptability across different fields are valuable. ...
This paper proposes a methodology on reliability and cost minimization of the electricity produced by Renewable energy sources. The approach is to use Hybrid Renewable Energy Systems, which involves integrating various renewable sources. The objective of this study is to address these challenges by HRES method in the state of Karnataka. In Karnataka, there has been large scale curtailment of renewable energy generation due to the various structural issues. This study would give a solution to reliability and cost related issues faced by this state. For the study a Mathematical Model has been developed using Feed Forward Neural Network (FNN) and Chemical Reaction Optimization algorithms. Chemical reaction Optimization model is chosen to remove biases of the Artificial Neural Network Model of FNN. This model will be used to simulate interaction between Hybrid Renewable Energy Systems. The result of the study will provide a power system with high & optimized renewable energy output in the state of Karnataka, India which will be reliable and cost effective.
... The third category includes chemistry-based algorithms that are inspired by the laws of chemistry. Some of them are the artificial chemical reaction algorithm (ACRO) [18], gases brownian motion optimization (GBMO) [19], artificial chemical process (ACP) [20], and chemotherapy science algorithm (CSA) [21]. The fourth category includes math's-based algorithms, which are inspired by mathematical rules. ...
... The diode current ( 1 and 2 ) and shunt resistor current ( ℎ ) in this equation are calculated by the formula in Equations (14), (15) and (16), respectively. The extended formula of the output current is given in Equation (17) and the objective function of this model is given in Equation (18). ...
... The diode current (I d1 and I d2 ) and shunt resistor current (I sh ) in this equation are calculated by the formula in Equations (14), (15) and (16), respectively. The extended formula of the output current is given in Equation (17) and the objective function of this model is given in Equation (18). ...
The carnivorous plant algorithm (CPA), which was recently proposed for solving optimization problems, is a population-based optimization algorithm inspired by plants. In this study, the exploitation phase of the CPA was improved with the teaching factor strategy in order to achieve a balance between the exploration and exploitation capabilities of CPA, minimize getting stuck in local minima, and produce more stable results. The improved CPA is called the I-CPA. To test the performance of the proposed I-CPA, it was applied to CEC2017 functions. In addition, the proposed I-CPA was applied to the problem of identifying the optimum parameter values of various solar photovoltaic modules, which is one of the real-world optimization problems. According to the experimental results, the best value of the root mean square error (RMSE) ratio between the standard data and simulation data was obtained with the I-CPA method. The Friedman mean rank statistical analyses were also performed for both problems. As a result of the analyses, it was observed that the I-CPA produced statistically significant results compared to some classical and modern metaheuristics. Thus, it can be said that the proposed I-CPA achieves successful and competitive results in identifying the parameters of solar photovoltaic modules.
... The second main branch of meta-heuristics is physicsbased techniques, which mimic physical rules. Popular algorithms include Gravitational Local Search Algorithm (GLSA) [10], Big-Bang Big-Crunch (BBBC) [11], Gravitational Search Algorithm (GSA) [12], Charged System Search (CSS) [13], Central Force Optimization (CFO) [14], Artificial Chemical Reaction Optimization Algorithm (ACROA) [15], Black Hole (BH) algorithm [16], Ray Optimization (RO) algorithm [17], Small-World Optimization Algorithm (SWOA) [18], Galaxy-based Search Algorithm (GbSA) [19], and Curved Space Optimization (CSO) [20]. These algorithms use a random set of search agents that move and communicate according to physical rules, such as gravitational force, ray casting, electromagnetic force, and inertia. ...
... The time of measurement is chosen randomly within the interval [0,t ul ] for each particle and in each dimension, where t ul is the upper limit of the time sampling range and typically is chosen as the period of oscillation. The iterative change in parameter t is defined as (15) ...
Numerical optimization techniques are widely used in a broad area of science and technology, from finding the minimal energy of systems in Physics or Chemistry to finding optimal routes in logistics or optimal strategies for high speed trading. In general, a set of parameters (parameter space) is tuned to find the lowest value of a function depending on these parameters (cost function). In most cases the parameter space is too big to be completely searched and the most efficient techniques combine stochastic elements (randomness included in the starting setting and decision making during the optimization process) with well designed deterministic process. Thus there is nothing like a universal best optimization method; rather than that, different methods and their settings are more or less efficient in different contexts. Here we present a method that integrates Particle Swarm Optimization (PSO), a highly effective and successful algorithm inspired by the collective behavior of a flock of birds searching for food, with the principles of Harmonic Oscillators. This physics-based approach introduces the concept of energy, enabling a smoother and a more controlled convergence throughout the optimization process. We test our method on a standard set of test functions and show that in most cases it can outperform its natural competitors including the original PSO as well as the broadly used COBYLA and Differential Evolution optimization methods.
... Gravitational search algorithm [25] is a notable example in this category developed based on Newton's law of gravity. Additional examples include artificial chemical reaction optimization algorithm [26], atom search optimization (ASO) [27], quantum Henry gas solubility optimization algorithm [28], ions motion algorithm [29], water evaporation optimization algorithm [30], and water cycle algorithm [31]. ...
... Furthermore, the problem is subject to four constraints [Equation (25)]. The admissible ranges of decision variables are bounded [Equation (26)]. Table 3 presents the results obtained by five successful variants of the CARO and six metaheuristic algorithms in the pressure vessel design problem. ...
This study introduces a novel metaheuristic algorithm of optimization named Chaotic Artificial Rabbits Optimization (CARO) algorithm for resolving engineering design problems. In the newly introduced CARO algorithm, ten different chaotic maps are used with the recently presented Artificial Rabbits Optimization (ARO) algorithm to manage its parameters, eventually leading to an improved exploration and exploitation of the search. The CARO algorithm and familiar metaheuristic competitor algorithms were experimented on renowned five mechanical engineering problems of design, in brief; pressure vessel design, rolling element bearing design, tension/compression spring design, cantilever beam design and gear train design. The results indicate that the CARO is an outstanding algorithm compared with the familiar metaheuristic algorithms, and equipped with the best-optimized parameters with the minimal deviation in each case study. Metaheuristic algorithms are utilized to succeed in an optimal design in engineering problems targeting to achieve lightweight designs. In this present study, the optimum design of a vehicle brake pedal piece was achieved through topology and shape optimization methods. The brake pedal optimization problem in terms of the mass minimization is solved properly by using the CARO algorithm in comparison to familiar metaheuristic algorithms in the literature. Consequently, results indicate that the CARO algorithm can be effectively utilized in the optimal design of engineering problems.
... Researchers lean toward HONNs because of their unique properties, such as large memory capacities, fast computation speeds, easy input-output mapping, and quick learning times. Meanwhile, evolutionary algorithms like genetic algorithm (GA) [9], particle swarm optimization (PSO) [10], monarch butterfly optimization (MBO) [11,12], [13], teaching-learning based optimization (TLBO) [14,15],follow the leader (FTL), cuckoo search (CS) [16], harmony search (HS) [17,18], honey bees mating optimization (HBMO), artificial bees colony (ABC) [19,20], firefly algorithm (FFA) [21], fireworks algorithm (FWA) [22], grey wolf optimization (GWO) [23,24], gravitational search algorithm (GSA) [25], ant colony optimization (ACO),chemical reaction optimization (CRO) [26], black hole optimization (BHO) [27], immune algorithm (IA), differential evolution (DE), plant grow optimization (PGO), genetic network programming (GNP) [28], bat algorithm (BA) and social spider optimization (SSO) [29], covariance matrix adaptation evolutionary strategy (CMA-CS) [30], Accelerated gray Wolf Optimization (AGWO) [31] and others are meant to automate the problem-solving skills of nature. Recently, the concept of combining ANN with an evolutionary algorithm has been expanding at a rapid rate. ...
... Alatas [26] in 2011 developed CRO algorithm which is persuaded by the guidelines of compound responses. Chemical reaction normally transforms substances from an unsteady state to a steady state. ...
... Physics-based optimization mimics physical laws for optimization. Simulated Annealing (SA) [5], Gravitational Search Algorithm (GSA) [6], Artificial Chemical Reaction Optimization Algorithm (ACROA) [7], Heat Transfer Search (HTS) [8], and Henry Gas Solubility Optimization (HGSO) [9] are considered well-known physics-based techniques. ...
One of the most recent algorithms called Advanced Jaya Algorithm (A-JA) is a combination of evolutionary technique, swarm-based metaheuristic and population-based approach. In this study, A-JA is investigated as an optimization method for engineering problems found in the literature. To assess the capability of A-JA, ten design problems that have different mathematical models were employed. In addition, the same problems were solved using popular algorithms such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), as well as less familiar metaheuristics like Harris Hawk Optimization (HHO) and Grey Wolf Optimization (GWO). The results showed that A-JA produces efficient outcomes, such as the ability to establish an effective equilibrium between exploration and exploitation and to escape from local optimums. Thus, the results indicate that A-JA often reports better solutions than other optimization algorithms.
... This enables them to effectively navigate complex solution landscapes and escape local optima. Examples of these algorithms include simulated annealing (Kirkpatrick et al. 1983), big-bang big-crunch (BBBC) (Erol and Eksin 2006), gravitational search algorithm (GSA) (Rashedi et al. 2009), charged system search (CSS) (Kaveh and Talatahari 2010), central force optimization (CFO) (Formato 2007), and artificial chemical reaction optimization algorithm (ACROA) (Alatas 2011). 3. Human-mimetic algorithms are based on human behavior. ...
The Bald Eagle Search (BES) algorithm is an innovative population-based method inspired by the intelligent hunting behavior of bald eagles. While BES shows promise, it faces challenges such as susceptibility to local optima and imbalances between exploration and exploitation phases. To address these limitations, this paper introduces the Multi-Strategy Boosted Bald Eagle Search (MBBES) algorithm. MBBES enhances the original BES by incorporating an adaptive parameter, two distinct mutation strategies, and replacing the swoop stage with a fall stage. We rigorously evaluate MBBES against classic and improved algorithms using the CEC2014 and CEC2017 test sets. The experimental results demonstrate that MBBES significantly improves the ability to escape local optima and achieves superior convergence accuracy. Moreover, MBBES ranks first according to the Friedman test, outperforming its counterparts in solving five practical engineering problems and three MLP classification problems, underscoring its effectiveness in real-world optimization scenarios. These findings indicate that MBBES not only surpasses BES but also sets a new benchmark in optimization performance.
... The artificial chemical reaction optimization algorithm (ACROA) [32], gas Brownian motion optimization (GBMO) [33] and Henry gas solubility optimization (HGSO) [34] are examples of chemistry-based MAs that also rely on important physics concepts such as Brownian motion and Henry's law. ACROA simulates interactions between chemical reactants: the positions of search agents correspond to concentrations and potentials of reactants and they are no longer perturbed when no more reactions can take place. ...
Metaheuristic algorithms (MAs) now are the standard in engineering optimization. Progress in computing power has favored the development of new MAs and improved versions of existing methods and hybrid MAs. However, most MAs (especially hybrid algorithms) have very complicated formulations. The present study demonstrated that it is possible to build a very simple hybrid metaheuristic algorithm combining basic versions of classical MAs, and including very simple modifications in the optimization formulation to maximize computational efficiency. The very simple hybrid metaheuristic algorithm (SHGWJA) developed here combines two classical optimization methods, namely the grey wolf optimizer (GWO) and JAYA, that are widely used in engineering problems and continue to attract the attention of the scientific community. SHGWJA overcame the limitations of GWO and JAYA in the exploitation phase using simple elitist strategies. The proposed SHGWJA was tested very successfully in seven “real-world” engineering optimization problems taken from various fields, such as civil engineering, aeronautical engineering, mechanical engineering (included in the CEC 2020 test suite on real-world constrained optimization problems) and robotics; these problems include up to 14 optimization variables and 721 nonlinear constraints. Two representative mathematical optimization problems (i.e., Rosenbrock and Rastrigin functions) including up to 1000 variables were also solved. Remarkably, SHGWJA always outperformed or was very competitive with other state-of-the-art MAs, including CEC competition winners and high-performance methods in all test cases. In fact, SHGWJA always found the global optimum or a best cost at most 0.0121% larger than the target optimum. Furthermore, SHGWJA was very robust: (i) in most cases, SHGWJA obtained a 0 or near-0 standard deviation and all optimization runs practically converged to the target optimum solution; (ii) standard deviation on optimized cost was at most 0.0876% of the best design; (iii) the standard deviation on function evaluations was at most 35% of the average computational cost. Last, SHGWJA always ranked 1st or 2nd for average computational speed and its fastest optimization runs outperformed or were highly competitive with their counterpart recorded for the best MAs.
... These optimization strategies typically mimic physical principles. Some of the most popular algorithms in this category include Gravitational Local Search (GLSA) [66], Small-World Optimization Algorithm (SWOA) [67], Big-Bang Big-Crunch (BBBC) [68], Central Force Optimization (CFO) [69], Charged System Search (CSS) [70], Galaxybased Search Algorithm (GbSA) [71], Artificial Chemical Reaction Optimization Algorithm (ACROA) [72], Curved Space Optimization (CSO) [73], Ray Optimization (RO) [74], and Black Hole (BH) [75]. Unlike evolutionary algorithms (EAs), these algorithms utilize a random selection of search processes that adhere to physical laws to navigate and interact within the search space. ...
Numerous studies have explored pricing and lot-sizing strategies for various payment methods, but most have focused primarily on the buyer's perspective. This study, however, approaches these strategies from a different perspective, incorporating key and relevant factors often overlooked. The volume of sales increases when a seller accepts a buyer's credit. However, it reduces sales volume when a seller requests a buyer make a payment in advance. To boost sales and profitability, a vendor occasionally provides a price reduction in exchange for a down payment. Demanding a down payment from a customer earns interest and carries without any risk of default. When a vendor offers customers the option to pay with credit, a higher delay payment period facility plan may boost sales volume, but it also increases the risk of default. To maximize profit per unit of time, the vendor aims to simultaneously determine the optimal selling price, replenishment schedule, and payment method. This is achieved by comparing and calculating the vendor's profit per time unit for credit, cash, and advance payment options. This is done by comparing and calculating the seller's profit for each piece of time for credit, cash, and advance payments. The following managerial impacts are highlighted by means of numerical analyses: (1) A particular payment type, among the three available options, yields the seller's highest profit under certain conditions. (2) It is vitally crucial for a vendor to provide a price reduction if an advance payment is required. (3) Advance payment results in higher profit than delayed payment if sales volume does not significantly fall while switching from credit to advance payments, or vice versa. To solve the optimization problem, a popular metaheuristic algorithm (viz., Grey Wolf Optimizer) is used and finally performed a post optimality analysis for making a fruitful conclusion.
... Simulated Annealing (SA) [41] Kirkpatrick et al. 1983 Magnetic Optimization Algorithm (MOA) [56] Tayaraniet al. 2008 Gravitational Search Algorithm (GSA) [42] Rashedi et al. 2009 Artificial Chemical Reaction Optimization (ACRO) [43] Alatas 2011 Lightning Search Algorithm (LSA) [57] Mirjalili 2015 Sine Cosine Optimization (SCA) [44] Tanyildizi et al. 2016 Golden Sine Algorithm (GSA) [58] Kaveh et al. 2017 Thermal Exchange Optimization (TEO) [45] Abualigah et al. 2017 Kepler Optimization Algorithm (KOA) [46] Basset et al. 2023 ...
Northern Goshawk Optimization (NGO) is an efficient optimization algorithm, but it has the drawbacks of easily falling into local optima and slow convergence. Aiming at these drawbacks, an improved NGO algorithm named the Multi-Strategy Improved Northern Goshawk Optimization (MSINGO) algorithm was proposed by adding the cubic mapping strategy, a novel weighted stochastic difference mutation strategy, and weighted sine and cosine optimization strategy to the original NGO. To verify the performance of MSINGO, a set of comparative experiments were performed with five highly cited and six recently proposed metaheuristic algorithms on the CEC2017 test functions. Comparative experimental results show that in the vast majority of cases, MSINGO’s exploitation ability, exploration ability, local optimal avoidance ability, and scalability are superior to those of competitive algorithms. Finally, six real world engineering problems demonstrated the merits and potential of MSINGO.
... Eskandar et al. introduced the Water Cycle Algorithm (WCA), modeled after the water cycle process [40]. Other notable PhAs include the Gravitational Search Algorithm (GSA) [41], Artificial Chemical Reaction Optimization Algorithm (ACROA) [42], Runge-Kutta Optimization Algorithm (RUN) [43], and Kepler Optimization Algorithm (KOA) [44]. ...
In response to the issues of local optima entrapment, slow convergence, and low optimization accuracy in Butterfly optimization algorithm (BOA), this paper proposes a hybrid Butterfly and Newton–Raphson swarm intelligence algorithm based on Opposition-based learning (BOANRBO). Firstly, by Opposition-based learning, the initialization strategy of the butterfly algorithm is improved to accelerate convergence. Secondly, adaptive perception modal factors are introduced into the original butterfly algorithm, controlling the adjustment rate through the adjustment factor α to enhance the algorithm's global search capability. Then, the exploration probability pp is dynamically adjusted based on the algorithm's runtime, increasing or decreasing exploration probability by examining changes in fitness to achieve a balance between exploration and exploitation. Finally, the exploration capability of BOA is enhanced by incorporating the Newton–Raphson-based optimizer (NRBO) to help BOA avoid local optima traps. The optimization performance of BOANRBO is evaluated on 65 standard benchmark functions from CEC-2005, CEC-2017, and CEC-2022, and the obtained optimization results are compared with the performance of 17 other well-known algorithms. Simulation results indicate that in the 12 test functions of CEC-2022, the BOANRBO algorithm achieved 8 optimal results (66.7%). In CEC-2017, out of 30 test functions, it obtained 27 optimal results (90%). In CEC-2005, among 23 test functions, it secured 22 optimal results (95.6%). Additionally, experiments have validated the algorithm’s practicality and superior performance in 5 engineering design optimization problems and 2 real-world problems.
... It finds a fast solution from a pool of prospects and postulates a moderately proximate way out to a given multifarious problem in a convincing manner [46]. ...
With advent of Internet of Things (IoT) an exponential growth has been observed in recent times towards the use of fifth generation (5G) network to share data among anything and even everything around connected in billions. The exchange of large amount of data by these devices or objects accumulates network overhead in the IoT infrastructure in terms of energy, routing, battery charge, data rate, packet delivery/loss rate, availability, interoperability, congestion, scalability, cost and security. Hence it is highly essential to project optimal solutions to uphold thereby the quality of service (QoS) in available network. This study provides a thorough literature survey of diverse optimization techniques in IoT aided wireless networks like Mobile Ad-hoc NETwork (MANET) driven Internet of Mobile Things (IoMobT), Vehicular Ad-hoc NETwork (VANET) driven Internet of Vehicles (IoV), Flying Ad-hoc NETwork (FANET) driven Internet of Flying Things (IoF), Robot Ad-hoc NETwork (RANET) enabled Internet of Robots (IoR), Ship Ad-hoc NETwork (SANET) driven Internet of Ships (IoS) and Underwater or Underground Ad-hoc NETwork (UANET) in Internet of Underwater or Underground Things (IoU). It categorizes papers based on the issues resolved by the examined works and optimization strategies employed and then it contrasts and condenses the salient characteristics of each kind of publication. It also even sketches a preview of IoT along with its evolving trends and cutting-edge-solutions for improving QoS. Our survey attempts to give readers a better grasp of the principles behind various computing models and to examine QoS network optimization strategies across a range of IoT models.
... Likewise, chemistry-based meta-heuristic algorithms are based on molecules' chemical reactions and features. These are LARES: artificial chemical processes (ACP) (Irizarry, 2004), artificial chemical reaction optimization (ACRO) (Alatas, 2011), and gas Brownian motion optimization (GBMO) (Abdechiri et al., 2013). ...
This paper introduces a disruptive strategy, namely a lizard-hunting approach, into the classical Moth Flame Optimization (MFO) algorithm. The conventional MFO emulates a moth's navigation pattern around artificial light at night but tends to face stagnation due to the flame's exploitative tendencies, often getting trapped in local optima, particularly in higher-dimensional problems. The research motivation stems from the need to disrupt small groups stuck at various local optima after a certain number of iterations. To address the limitations of the existing MFO, the proposed Lizard-Moth-Flame Optimization (L-MFO) algorithm is put forth. In L-MFO, moth positions are classified into outlier and non-outlier categories using a clustering method in each iteration. Following this categorization, non-outliers are divided into highly and less densely populated subgroups, with the densely crowded group considered closer to the solution. However, a distinctive aspect of the lizard's behaviour in L-MFO is its inclination towards the less crowded group, reflecting a slower update. When a moth detects a lizard within its range and at the same angle, moths within the group either flee in the opposite direction or move towards a densely crowded group. This strategic response mitigates the issue of stagnation, enhancing the algorithm's overall performance. The proposed L-MFO algorithm undergoes a comprehensive evaluation by being compared with other state-of-the-art meta-heuristic algorithms. The assessment involves testing on twenty-three CEC-2005 benchmarks across different dimensions (10, 30, 50, 100, 500, 1000, 2000, and 5000), eight engineering problems, and 36 CEC-2017 benchmark functions with 10, 50 and 100 dimensions. The robustness of the algorithm is examined through convergence and divergence analysis, Wilcoxon signed-rank test, Two-tailed Mann-Whitney U test, and boxplot analysis. The experimental and statistical results consistently demonstrate the superior performance of L-MFO over other algorithms.
... A natural process of changing unstable molecules into stable molecules is called a chemical reaction. Another chemistry-based algorithm is artificial chemical reaction optimization algorithm (ACROA) 49 . ...
In recent years, many researchers have made a continuous effort to develop new and efficient meta-heuristic algorithms to address complex problems. Hence, in this study, a novel human-based meta-heuristic algorithm, namely, the learning cooking algorithm (LCA), is proposed that mimics the cooking learning activity of humans in order to solve challenging problems. The LCA strategy is primarily motivated by observing how mothers and children prepare food. The fundamental idea of the LCA strategy is mathematically designed in two phases: (i) children learn from their mothers and (ii) children and mothers learn from a chef. The performance of the proposed LCA algorithm is evaluated on 51 different benchmark functions (which includes the first 23 functions of the CEC 2005 benchmark functions) and the CEC 2019 benchmark functions compared with state-of-the-art meta-heuristic algorithms. The simulation results and statistical analysis such as the t-test, Wilcoxon rank-sum test, and Friedman test reveal that LCA may effectively address optimization problems by maintaining a proper balance between exploitation and exploration. Furthermore, the LCA algorithm has been employed to solve seven real-world engineering problems, such as the tension/compression spring design, pressure vessel design problem, welded beam design problem, speed reducer design problem, gear train design problem, three-bar truss design, and cantilever beam problem. The results demonstrate the LCA’s superiority and capability over other algorithms in solving complex optimization problems.
... Physics-based algorithms mainly include Multi-Verse Optimization (MVO) [32], Special Relativity Search (SRS) algorithm [33] and Kepler Optimization Algorithm (KOA) [34]. Chemical-based algorithms mainly include Gas Brownian Motion Optimization (GBMO) [35], Kinetic Gas Molecular Optimization (KGMO) [36] and Artificial Chemical Reaction Optimization Algorithm (ACROA) [37]. Human-based algorithms are mainly inspired by human behaviors, such as Alpine Skiing Optimization (ASO) [38] and Group Teaching Optimization Algorithm (GTOA) [39]. ...
In response to the shortcomings of Dwarf Mongoose Optimization (DMO) algorithm, such as insufficient exploitation capability and slow convergence speed, this paper proposes a multi-strategy enhanced DMO, referred to as GLSDMO. Firstly, we propose an improved solution search equation that utilizes the Gbest-guided strategy with different parameters to achieve a trade-off between exploration and exploitation (EE). Secondly, the Lévy flight is introduced to increase the diversity of population distribution and avoid the algorithm getting stuck in a local optimum. In addition, in order to address the problem of low convergence efficiency of DMO, this study uses the strong nonlinear convergence factor Sigmaid function as the moving step size parameter of the mongoose during collective activities, and combines the strategy of the salp swarm leader with the mongoose for cooperative optimization, which enhances the search efficiency of agents and accelerating the convergence of the algorithm to the global optimal solution (Gbest). Subsequently, the superiority of GLSDMO is verified on CEC2017 and CEC2019, and the optimization effect of GLSDMO is analyzed in detail. The results show that GLSDMO is significantly superior to the compared algorithms in solution quality, robustness and global convergence rate on most test functions. Finally, the optimization performance of GLSDMO is verified on three classic engineering examples and one truss topology optimization example. The simulation results show that GLSDMO achieves optimal costs on these real-world engineering problems.
... It's noteworthy that there are meta-heuristics that don't belong to the four categories mentioned above, as they originate from various fields such as mathematics, including the Sine Cosine Algorithm (SCA) [85], Runge Kutta method (RUN) [86], and Arithmetic Optimization Algorithm (AOA) [87]; sports, including Volleyball Premier League (VPL) [88], Football Game-Based Optimizer (FGBO) [89], and Puzzle Optimization Algorithm (POA) [90]; chemistry, including Chemical Reaction Optimization (CRO) [91], Artificial Chemical Reaction Optimization Algorithm (ACROA) [92], Gases Brownian Motion Optimization (GBMO) [93]; and diseases, including Coronavirus Herd Immunity optimizer (CHIO) [94], Ebola Optimization Search Algorithm (EOSA) [95], and Coronavirus Optimization Algorithm (COVIDOA) [96]. ...
In this study, we introduce the pied kingfisher optimizer (PKO), a novel swarm-based meta-heuristic algorithm that draws inspiration from the distinctive hunting behavior and symbiotic relationships observed in pied kingfishers in the natural world. The PKO algorithm is structured around three distinct phases: perching/hovering for prey (exploration/diversification), diving for prey (exploitation/intensification), and fostering symbiotic relations. These behavioral aspects are translated into mathematical models capable of effectively addressing a wide array of optimization challenges across diverse search spaces. The algorithm’s performance is rigorously evaluated across thirty-nine test functions, which encompass various unimodal, multimodal, composite, and hybrid ones. Additionally, eight real-world engineering optimization problems, including both constrained and unconstrained scenarios, are considered in the assessment. To gauge PKO’s efficacy, it is subjected to a comparative analysis against 3 categories of rival optimizers. The 1st category comprises well-established and widely-cited optimizers such as particle swarm optimization and genetic algorithm. The 2nd category encompasses recently published algorithms, including Harris Hawks optimization, Whale optimization algorithm, sine cosine algorithm, Grey Wolf optimizer, gravitational search algorithm, and moth-flame optimization. The 3rd category includes advanced algorithms, such as covariance matrix adaptation evolution strategy and Ensemble Sinusoidal Differential Covariance Matrix Adaptation with Euclidean Neighborhood (LSHADE-cnEpSin). The comparative analysis employs various performance metrics, including the Friedman mean rank and the Wilcoxon rank-sum test, to reveal PKO’s effectiveness and efficiency. The overall results highlight PKO’s exceptional ability to tackle intricate optimization problems characterized by challenging search spaces. PKO demonstrates superior exploration and exploitation tendencies while effectively avoiding local optima. The source code for the PKO algorithm is publicly accessible at https://www.mathworks.com/matlabcentral/fileexchange/160043-pied-kingfisher-optimizer-pko.
... This enhancement involves adding two additional steps to the original ECPO algorithm: "ionization" and "electron exchange". Both steps are inspired by chemical phenomena, specifically mimicking chemical reactions, as observed in previous research [51][52][53]. ...
Designing an onshore wind farm layout poses several challenges, including the effects of terrain and landscape characteristics. An accurate model should be developed to obtain the optimal wind farm layout. This study introduces a novel metaheuristic algorithm called Modified Electric Charged Particles Optimization (MECPO) to maximize wind farms’ annual energy production (AEP) by considering the different terrain and landscape characteristics of the sites. Some non-uniform scenarios are applied to the optimization process to find the best combination of decision variables in the wind farm design. The study was initiated by a uniform wind farm layout optimization employing identical wind turbine hub heights and diameters. Following this, these parameters underwent further optimization based on some non-uniform scenarios, with the optimal layout from the initial uniform wind farm serving as the reference design. Three real onshore sites located in South Sulawesi, Indonesia, were selected to validate the performance of the proposed algorithm. The wind characteristics for each site were derived from WAsP CFD, accounting for the terrain and landscape effects. The results show that the non-uniform wind farm performs better than its uniform counterpart only when using varying hub heights. Considering the impacts of the terrain and landscape characteristics, it is observed that sites with a higher elevation, slope index, and roughness length exhibit a lower wake effect than those with lower ones. Moreover, the proposed algorithm, MECPO, consistently outperforms other algorithms, achieving the highest AEP across all simulations, with a 100% success rate in all eight instances. These results underscore the algorithm’s robustness and effectiveness in optimizing wind farm layouts, offering a promising avenue for advancing sustainable wind energy practices.
... These rules usually constrain the interaction of searching individuals in such methods. Moreover, most of these laws are related to gravity, electromagnetic force, chemical reaction, etc. Examples of these algorithms include the Big Bang-Big Crunch (BBBC) [44], the gravitational search algorithm (GSA) [45], the chemical reaction optimization (CRO) [46], the artificial chemical reaction optimization algorithm (ACROA) [47], the black hole (BH) algorithm [48], the multi-verse optimizer (MVO) [49], the thermal exchange optimization (TEO) [50], the Archimedes optimization algorithm (AOA) [51], the equilibrium optimizer (EO) [52], the string theory algorithm (STA) [53], and the atomic orbit search (AOS) [54]. ...
This paper proposes the Love Evolution Algorithm (LEA), a novel evolutionary algorithm inspired by the stimulus–value–role theory. The optimization process of the LEA includes three phases: stimulus, value, and role. Both partners evolve through these phases and benefit from them regardless of the outcome of the relationship. This inspiration is abstracted into mathematical models for global optimization. The efficiency of the LEA is validated through numerical experiments with CEC2017 benchmark functions, outperforming seven metaheuristic algorithms as evidenced by the Wilcoxon signed-rank test and the Friedman test. Further tests using the CEC2022 benchmark functions confirm the competitiveness of the LEA compared to seven state-of-the-art metaheuristics. Lastly, the study extends to real-world problems, demonstrating the performance of the LEA across eight diverse engineering problems. Source codes of the LEA are publicly available at https://ww2.mathworks.cn/matlabcentral/fileexchange/159101-love-evolution-algorithm.
... field with other nature inspired meta-heuristic optimization algorithms. Further, WOA provides competitive results in the different sectors like medical, engineering, chemical science [14,15]. The demerits of WOA are (a) suffered as a result of entrapping at the local optima and (b) Slow convergence rate (c) not goodly maintained balanced between diversification and intensification. ...
The Whale Optimization Algorithm (WOA) had widespread use across a wide variety of scientific and engineering domains for its simple and efficient behavior. However, it has some deficiencies, like slow convergence, faltering at local optima and not good at stability. To tackle these deficiencies, an improved variant of WOA, called F-WOA is introduced in this work. The suggested F-WOA uses crossover weight and the Fibonacci search principle, two efficient ways for maintaining a healthy harmony between searching on a global and regional scale, both of them speed up the convergence rate. The suggested method is tested on many benchmark functions and the IEEE CEC 2019 suite, and its research results are managed to compare with those of existing state-of-the-art methods to determine how well it performs. The Friedman rank test is also used in order to establish if or not the proposed F-WOA is scientifically superior to existing alternatives. The method has solved two types of engineering design problems. As per the outcomes of the experiments conducted, it appears that the proposed F-WOA is a viable alternative to other methods for achieving optimal results in a broad range of applicable optimization scenarios in the real world.
The development of new algorithms can aid researchers and professionals in resolving problems that were once unsolvable or discovering superior solutions to problems that were already settled. By recognizing the importance of continuous research on creating novel algorithms, this paper introduced a hybrid metaheuristic algorithm-h-PSOGNDO, which is a combination of Particle Swarm Optimization (PSO) and Generalized Normal Distribution Optimization (GNDO). The proposed algorithm utilizes the Particle Swarm Optimization’s strategy for exploitation and the Generalized Normal Distribution Optimization’s global search strategy for exploration. Through this combination, h-PSOGNDO is believed to be an effective algorithm that can promote the advantages of its parents’ algorithms. Different assessment methods are used to assess the proposed novel algorithm. First, the h-PSOGNDO is set to conduct experiments on two sets of mathematical functions, including twenty-eight IEEE CEC2017 and ten IEEE CEC2019 benchmark test functions, respectively. Then, the h-PSOGNDO algorithm is applied to a case study on the prediction of antimicrobial peptides’ toxicity to evaluate its performance on real-life problems. The statistical findings collected from both the test function sets and the case study show that the h-PSOGNDO algorithm works effectively, proving its astonishing ability to yield highly competitive outcomes for complex problems.
In conjunction with Chap. 1, this chapter discusses the further process of the optimization algorithms that focuses on the optimization phase of metaheuristic algorithm and discusses the applicable options for the essential tradeoff between exploration and exploitation search mechanisms.
This study proposes a novel physics-inspired metaheuristic algorithm named Forest Fire Ash Optimizer (FFA). Inspired by the characteristics of ash movement in different forest fire burning stages, intelligently transformed these regular behaviors into a variety of unique algorithmic mechanisms, including a four-population mechanism based on fitness and denseness partitioning, an inverse proportionality selection mechanism for elite influence, and an ash movement strategy grounded in actual physical phenomena. These components complement each other to enable flexible regulation of exploration and exploitation, i.e., the two phases are not merely sequential, but change dynamically depending on the search status of FFA agents. To verify the effectiveness of the proposed algorithm, FFA is qualitatively analyzed using the CEC-2022 test suite. Additionally, the classical standard test suite, CEC-2017 test suite, and CEC-2022 test suite are conducted to compare the performance of FFA with 9 advanced algorithms. The results demonstrate that FFA excels in performance, exhibiting high stability, flexibility, and robustness. Finally, FFA is applied to 9 classical and challenging real-world engineering optimization problems. The results indicate that, compared to the competing algorithms, FFA provides superior and more novel solutions, predicting its potential for in-depth applications in more engineering fields.
Optimization algorithms are continuously evolving and considered as an active multidiscipline research area to design scalable solutions for complex optimization problems. Literature witnesses the constant effort by researchers to improve existing optimization algorithms or to develop a new algorithm to deal with single and multiple objective problems. This research paper presents a novel population-based, metaheuristic bio-inspired optimization algorithm. The algorithm contrived the propagation concept of antibiotic-resistant bacteria named as antibiotic-resistant bacteria propagation (ARBP) algorithm where properties of bacteria to acquire antibiotic resistance over time are used as a base concept. The optimization algorithm imitates the two prime mechanisms of horizontal gene transfer—Conjugation Gene Transfer Mechanism (CGTM) and Transformation Gene Transfer Mechanism (TGTM) to propagate antibiotic-resistant bacteria. CGTM and TGTM are used to explore the search space to handle single and multiple objective optimization problems. Conjugation mechanism is used for exploration of search space and exploitation concept is driven by transformation mechanism. The efficiency and importance of the ARBP algorithm are validated on varying classical and complex benchmark functions. An extensive comparative study is performed to detail the effectiveness of ARBP over other well-known swarm and evolutionary algorithms. This comparative analysis clearly depicts that the performance of ARBP is superior in terms of finding a better solution with high convergence as compared to other considered algorithms.
With little or no human intervention, almost every object in Internet of Things (IOT) has ability to communicate, sense, and process information to make everything connected. Noise in complex environment has a great impact on machine-tomachine (M2M) interaction in IOT. Adaptive Wavelet Threshold Function-based M2M Gaussian Noise Removal Method is proposed in this paper. Firstly, a bilateral enhanced wavelet threshold function is derived based on the adjustable zeroing window. Further, threshold is used to construct a bilateral enhanced wavelet threshold function, which can eliminate the oscillations in the existing wavelet threshold function. This ensures that there is no break in wavelet coefficients during the reconstruction process and that stable wavelet decomposition and reconstruction can be achieved. The signal-to-noise ratio (SNR) of a signal is estimated based on the variance of the noise-containing signal, and the zeroing window parameters are adjusted adaptively according to the SNR value to eliminate the noisy wavelet coefficients and improve the denoising performance. In addition, when the wavelet coefficients are reconstructed, the proposed algorithm can select a suitable threshold function according to a particular threshold value, which improves the robustness of the algorithm. “Doppler” and “Bumps” standard test signals are used as interactive signals to simulate the proposed algorithms. For “Doppler” signals, the SNR, RMSE and Noise Suppression Ratio (NSR) of Adaptive Wavelet Threshold Function (AWTF) increased by 8.48%, 42.59% and 1.69%, respectively, compared with the GDES+ABC algorithm. For “Bumps” signal, the SNR, RMSE and NSR of AWTF are improved by 11.67%, 24.46% and 2.99% respectively compared with GDES+ABC algorithm. In addition, we also use different modulated signals to carry out real field experiments in Qingdao cruise ship home port, which proves the effectiveness of the proposed algorithm.
In the path planning of UAVs, autonomous decision-making and control are challenging tasks in the uncertain 3D environment consisting of static and dynamic obstacles. Hence, the selection of appropriate path-planning approaches is essential. In the proposed work, we have considered the meta-heuristic approaches only for an in-depth review. Metaheuristic approaches have been remarkably known for solving complex problems, optimal solutions, and lesser computational complexity compared to deterministic approaches that produce an inefficient solution. An in-depth review has been made by considering the approaches used for path planning, their advantages, disadvantages, applications, the type of time domain (offline or online), type of environment (simulation or real time), hybridization with other approaches, single or multiple UAV system, and obstacle handled (static or dynamic). It is observed that current meta-heuristic methods face constraints like inadequate convergence rates, entrapment in local optima, and complex operations, necessitating continuous development of novel approaches. Implementation of path-planning approaches are very much limited to simulation study over experimental analysis. Hybrid algorithms emerge as a potential solution for tackling these hurdles and optimizing UAV navigation, particularly in dynamic environments involving multiple UAVs. The paper highlights key research gaps, trends, along with prospects in the field of research.
The use of trade credit finance is becoming more widely acknowledged as a crucial approach to improving inventory system profitability. We review an inventory model with depending on permitted payment delays for which, if the retailer place an orders higher than or equal to a predefined quantity S1, then the supplier will provide a fully pay in later facility of ξ periods (i.e., there will be no charge of interest until ξ). On the other hand the retailer need to pay a partial amount of payment to the supplier if the order quantity is less than S1, and the remaining amount may be deferred for up to ξ periods. Main objective of this study is to investigate the inventory model with different situations under delayed payment facility. In addition, determining the product's demand also involves taking into account the item's greenness and selling price. We have also considered the fact that the cost of buying is influenced by the product's degree of greenness. We employ the meta heuristic algorithm Grey Wolf Optimizar (GWO) to assist us in solving the problem, and we compare the outcomes with the aid of a few other algorithms (Whale optimisation algorithm (WOA) and Artificial electric field algorithm (AEFA)). In the end, we resolve several numerical cases to support the model. The concavity of the desired function is graphically displayed using MATLAB software.
The current study is defined by two main aims. An effective strategy for improving local search is to combine the Set Algebra-Based Heuristic Algorithm (SAHA) algorithm with the Nelder-Mead simplex method. The approach outlined above, referred to as the Improved SAHA (ISAHA), and has the ability to produce superior outcomes. The multiplicative functional link artificial neural network (MFLANN) is an improved version of the functional link artificial neural network that promotes exploration by replacing the summing unit in the output layer with a multiplication unit. Moreover, the combination of ISAHA and MFLANN results in the development of ISAHA-MFLANN, a sophisticated hybrid forecasting model. The main assessment of the hybrid model rests on its ability to forecast complex and dynamic financial time series. It's possible to get around the problems that come with traditional learning-based MFLANN techniques by using MFLANN's advanced approximation features and ISAHA's resilient global search features together. Experimental verification using two stock market datasets and three currency exchange rates demonstrates the validity of the idea. The results show that the suggested improved SAHA hybrid learning works well at improving six standard benchmark functions. The ISAHA-MFLANN model is also statistically significant at accurately capturing the volatility that is inherent in the financial time series. In addition, it surpasses other models such as SAHA-MFLANN, Monarch Butterfly Optimization-MFLANN, Particle Swarm Optimization-MFLANN, Genetic Algorithm-MFLANN, Gradient Descent-MFLANN, Support Vector Machine (SVM), Multilayer Perceptron (MLP), and Auto Regressive Integrated Moving Average (ARIMA).
Snake Optimizer (SO) is a novel Meta-heuristic Algorithm (MA) inspired by the mating behaviour of snakes, which has achieved success in global numerical optimization problems and practical engineering applications. However, it also has certain drawbacks for the exploration stage and the egg hatch process, resulting in slow convergence speed and inferior solution quality. To address the above issues, a novel multi-strategy improved SO (MISO) with the assistance of population crowding analysis is proposed in this article. In the algorithm, a novel multi-strategy operator is designed for the exploration stage, which not only focuses on using the information of better performing individuals to improve the quality of solution, but also focuses on maintaining population diversity. To boost the efficiency of the egg hatch process, the multi-strategy egg hatch process is proposed to regenerate individuals according to the results of the population crowding analysis. In addition, a local search method is employed to further enhance the convergence speed and the local search capability. MISO is first compared with three sets of algorithms in the CEC2020 benchmark functions, including SO with its two recently discussed variants, ten advanced MAs, and six powerful CEC competition algorithms. The performance of MISO is then verified on five practical engineering design problems. The experimental results show that MISO provides a promising performance for the above optimization cases in terms of convergence speed and solution quality.
A Modified Leader-Advocate-Believer (LAB) algorithm is introduced in this paper. It builds upon the original LAB algorithm (Reddy et al. 2023), which is a socio-inspired algorithm that models competitive and learning behaviours within a group, establishing hierarchical roles. The proposed algorithm incorporates the roulette wheel approach and a reduction factor introducing inter-group competition and iteratively narrowing down the sample space. The algorithm is validated by solving the benchmark test problems from CEC 2005 and CEC 2017. The solutions are validated using standard statistical tests such as two-sided and pairwise signed rank Wilcoxon test and Friedman rank test. The algorithm exhibited improved and superior robustness as well as search space exploration capabilities. Furthermore, a Clustering-Based Search Space Reduction (C-SSR) method is proposed, making the algorithm capable to solve constrained problems. The C-SSR method enables the algorithm to identify clusters of feasible regions, satisfying the constraints and contributing to achieve the optimal solution. This method demonstrates its effectiveness as a potential alternative to traditional constraint handling techniques. The results obtained using the Modified LAB algorithm are then compared with those achieved by other recent metaheuristic algorithms.
Metaheuristic algorithms are often preferred for solving constrained engineering design optimization problems. The most important reason for choosing these algorithms is that they guarantee a satisfactory response within a reasonable time. The swarm intelligence-based manta ray foraging optimization algorithm (MRFO) is a metaheuristic algorithm proposed to solve engineering applications. In this study, the performance of MRFO is evaluated on 19 mechanical engineering optimization problems in the CEC2020 real-world constrained optimization problem suite. In order to increase the MRFO performance, three modifications are made to the algorithm; in this way, the enhanced manta ray foraging optimization (EMRFO) algorithm is proposed. The effects of the modifications made are analyzed and interpreted separately. Its performance has been compared with the algorithms in the literature, and it has been shown that EMRFO is a successful and preferable algorithm for this problem suite.
Meta-heuristic algorithms (MHAs) have gained popularity in recent years due to their successful results in solving a wide variety of scientific problems. They offer ease of use, fast implementation, and effective convergence toward the optimal solution. Although MHAs have been extensively tested in solving well-known mathematical benchmark problems with one or more dimensions as well as civil and mechanical engineering problems in their initial demonstrations, controller design problems are not typically considered. Furthermore, the literature lacks a standardized optimization process for controller design problems using MHAs. Due to variations in iteration numbers, population sizes, number of trials, objective functions, and insufficient analysis presented in research papers, it becomes challenging to compare and evaluate the controller design performance of MHAs in a successful and fair manner. This work aims to establish a standardized approach for evaluating the performance of MHAs in controller design by proposing a consistent function evaluation metric. To achieve this goal, we present the most comprehensive and comparative study of MHAs’ performance in controller design conducted to date. In this paper, we utilize two commonly used objective functions in controller design: Zwe Lee Gaing and Integral Time Absolute Error. Additionally, we employ a total of twenty algorithms, consisting of ten classical algorithms and ten recently popular algorithms. We evaluate the performance of these algorithms on the “automatic voltage regulation” electric power system problem, which serves as a widely used benchmark for meta-heuristically optimized controllers. We consider three different controllers with three (PID), five (FOPID), and seven (FOPIDD) parameters. The performance results of the selected algorithms are thoroughly discussed, considering various analysis techniques such as box plot analysis, convergence curves, and transient response performances, as well as statistical tests like Wilcoxon and Friedman tests. As a result, symbiotic organisms search, teaching–learning based optimization, chaos game optimization, supply–demand based optimization, and jellyfish search algorithms generally emerge as the best-performing algorithms across all optimization processes for the three types of controllers. Researchers interested in conducting further analysis and comparing the improved algorithms can access all the models,parameters, and codes used in this study from the provided link (https://www.mathworks.com/matlabcentral/fileexchange/161336-fractional-order-controller-optimization-for-avr).
There has been a lot of research on pricing and lot-sizing practices for different payment methods; however, the majority has focused on the buyer’s perspective. While accepting buyers’ credit conditions positively impacts sales, requesting advance payments from purchasers tends to have a negative effect. Additionally, requiring a down payment has been found to generate interest revenue for the supplier without introducing default risk. However, extending the credit period, along with offering delayed payment options, has the potential to increase sales volume, albeit with an elevated risk of defaults. Taking these payment schemes into account, this study investigates and compares the per-unit profit for sellers across three distinct payment methods: advance payment, cash payment, and credit payment. The consumption rate of the product varies non-linearly not only with the time duration of different payment options but also with the price and the level of greenness of the product. The utmost objective of this work is to determine the optimal duration associated with payment schemes, selling price, green level, and replenishment period to maximize the seller’s profit. The Teaching Learning Based Optimization Algorithm (TLBOA) is applied to address and solve three numerical examples, each corresponding to a distinct scenario of the considered payment schemes. Sensitivity analyses confirm that the seller’s profit is markedly influenced by the environmental sustainability level of the product. Furthermore, the seller’s profitability is more significantly affected by the selling price index compared to the indices of the payment scheme duration and the green level in the demand structure.
The present study introduces a novel nature-inspired optimizer called the Pine Cone Optimization algorithm (PCOA) for solving science and engineering problems. PCOA is designed based on the different mechanisms of pine tree reproduction, including pollination and pine cone dispersal by gravity and animals. It employs new and powerful operators to simulate the mentioned mechanisms. The performance of PCOA is analyzed using classic benchmark functions, CEC017 and CEC2019 as mathematical problems and CEC2006 and CEC2011 as engineering design problems. In terms of accuracy, the results show the superiority of PCOA to well-known algorithms (PSO, DE, and WOA) and new algorithms (AVOA, RW_GWO, HHO, and GBO). The results of PCOA are competitive with state-of-the-art algorithms (LSHADE and EBOwithCMAR). In terms of convergence speed and time complexity, the results of PCOA are reasonable. According to the Friedman test, PCOA’s rank is 1.68 and 9.42 percent better than EBOwithCMAR (second-best algorithm) and LSHADE (third-best algorithm), respectively. The authors recommend PCOA for science, engineering, and industrial societies for solving complex optimization problems.
In this study, an improved version of Aquila Optimizer (AO) known as EHAOMPA has been developed by using the Marine Predators Algorithm (MPA). MPA is a recent and well-behaved optimizer with a unique memory saving and FADs mechanism. At the same time, it suffers from various defects such as inadequate global search, sluggish convergence, and stagnation of local optima. However, AO has contented robust global exploration capability, fast convergence speed, and high search efficiency. Thus, the proposed EHAOMPA aims to complement the shortcomings of AO and MPA while bringing new features. Specifically, the representative-based hunting technique is incorporated into the exploration stage to enhance population diversity. At the same time, random opposition-based learning (ROBL) is introduced into the exploitation stage to prevent the optimizer from sticking to local optima. This study tests the performance of EHAOMPA's on twenty-three standard mathematical benchmark functions, 29 complex test functions from the CEC2017 test suite, six constrained industrial engineering design problems, and a CNN-hyperparameter optimization for COVID-19 CT-image detection problem. EHAOMPA is compared with four existing optimization algorithm types, achieving the best performance on both numerical and practical issues. Compared to other methods, the test function results demonstrate that EHAOMPA exhibits a more potent global search capability, a higher convergence rate, increased accuracy, and an improved ability to avoid local optima. The excellent experimental results in practical problems indicate that the developed EHAOMPA has great potential in solving real-world optimization problems. The combination of multiple strategies can effectively improve the performance of the algorithm. The source code of the EHAOMPA is publicly available at https://github.com/WangShuang92/EHAOMPA.
Single-solution-based optimization algorithms are computationally cheap yet powerful methods that can be used on various optimization tasks at minimal processing expenses. However, there is a considerable shortage of research in this domain, resulting in only a handful of proposed algorithms over the last four decades. This study proposes the Prism Refraction Search (PRS), a novel, simple yet efficient, single-solution-based metaheuristic algorithm for single-objective real-parameter optimization. PRS is a physics-inspired algorithm modeled on a well-known optimization paradigm in ray optics arising from the refraction of light through a triangular prism. The key novelty lies in its scientifically sound background that is supported by the well-established laws of physical optics. The proposed algorithm is evaluated on several numerical objectives, including 23 classical benchmark functions, the CEC-2017 test suite, and five standard real-world engineering design problems. Further, the results are analyzed using standard statistical tests to prove their significance. Extensive experiments and comparisons with state-of-the-art metaheuristic algorithms in the literature justify the robustness and competitive performance of the PRS algorithm as a lightweight and efficient optimization strategy.
Computational models are foundational concepts in computer science; many of these models such as P systems are based on natural biological processes. P systems represent a wide framework for a variety of concepts of data mining, as models of data clustering approaches. Data clustering is a technique for analyzing data based on its structure that is widely utilized for many applications. In this paper, the proposed model (PSO-MFM) has combined the Particle Swarm Optimization algorithm (PSO) with Mitochondrial Fusion Model to overcome some constraints of clustering techniques. The solving of clustering problem based on particle swarm is investigated in the proposed model when mutual dynamic rules are used. It can find the best cluster centers for a data set and improve clustering performance by utilizing the distributed parallel computing concept of mutual dynamic rules of mitochondrial fusion model. The comparative results demonstrate that the proposed strategy outperforms competition models when it comes to clustering accuracy, stability and the most efficient in time complexity.
Optimization is a method which is used in every field, such as engineering, space, finance, fashion market, mass communication, travelling, and also in our daily activities. In every field, everyone always wants to minimize or maximize something called the objective function. Traditional and modern optimization techniques or Meta-Heuristic (MH) optimization techniques are used to solve the objective functions. But the traditional optimization techniques fail to solve the complex and real-world optimization problem consisting of non-linear objective functions. So many modern optimization techniques have been proposed exponentially over the last few decades to overcome these challenges. This paper discusses a brief review of the different benchmark test functions (BTFs) related to existing MH optimization algorithms (OA). It discusses the classification of MH algorithms reported in the literature regarding swarm-based, human-based, physics-based, and evolutionary-based methods. Based on the last half-century literature, MH-OAs are tabulated in terms of the proposed year, author, and inspiration agent. Furthermore, this paper presents the MATLAB and python code web-link of MH-OA. After reading this review article, readers will be able to use MH-OA to solve challenges in their field.
African Vulture Optimization Algorithm (AVOA) is a nature-inspired optimization technique that draws its inspiration from the behaviours and foraging patterns of African vultures. Vultures are known for their exceptional scavenging abilities and their keen sense of locating carrion from vast distances. The AVOA algorithm aims to mimic these traits to solve complex optimization problems. Since its introduction by Abdollahzadeh et al. in 2021, AVOA has attracted a lot of attention from the researchers and has been extensively used to solve several optimization problems in a wide range of fields. This is because it outperforms other popular optimization algorithms in terms of execution time, convergence rate, and effectiveness. As a result, this study's goal is to present an up-to-date survey of this topic. This study encompasses the algorithm's evolution, modifications, and hybridizations with other techniques, showcasing its adaptability and potential for synergy. Furthermore, this study highlights the real-world applications of AVOA across diverse domains, showcasing its adaptability and effectiveness in solving complex problems. As nature-inspired optimization techniques gain impact for solving complex optimization problems, this study provides a valuable resource for researchers to acquire a comprehensive understanding of the AVOA, its evolution, and its potential applications in tackling complex optimization issues across various fields.
The tremendous increase in daily internet users leads to the explosion of on-demand requests over the cloud. It causes a burden in the cloud environment (diverse and complicated applications) for cloud services. The assignment of resources to the associated task varies based on the functioning of resources available across the cloud. It establishes the importance of task scheduling in cloud computing. Inadequate scheduling techniques address the issues of resource overuse and underuse (imbalance), resulting in service degradation (in the event of overuse) or cloud resource waste (in the case of underuse or underutilized). The primary idea is to eliminate an imbalance problem by employing an appropriate scheduling algorithm that may efficiently allocate jobs (of varying and complicated types) among cloud resources. The parameters which impact the activity mentioned above are resource utilization, reliability, makespan time, cost, energy consumption, availability, response time, and other critical performance indicator metrics. In order to create a productive cloud scheduling method, these matrices need to be optimized. Many state-of-the-art cloud task scheduling algorithms based on heuristic, meta-heuristic, and hybrid design have been presented and discussed in the literature as part of the study. This study presents a comprehensive assessment and classification of various scheduling systems and their benefits and drawbacks. Our detailed & comprehensive survey effort will serve as a stepping stone for new cloud computing researchers and aid in pursuing research in this direction.
This study introduces a new human-based meta-heuristic algorithm, the Learning Cooking Algorithm (LCA), based on the opposition-based learning (OBL) strategy, namely the Opposition-based Learning Cooking Algorithm (OLCA). The proposed OLCA algorithm consists of four stages: the first stage, where the OBL strategy is implemented to improve the initial population; the second stage, where children learn from their respective mothers; the third stage, where children and mothers learn from chefs; and the fourth stage, where OBL is applied again to update the population. The proposed OLCA has been examined over 23 test functions, and the OLCA outcomes are equated with several popular and top-performing optimization algorithms. The statistical outcomes, such as the average (Ave), standard deviation (Std), Wilcoxon rank-sum test, and [Formula: see text]-test, reveal that the outcomes of OLCA may effectively address optimization problems by maintaining a proper balance between exploitation and exploration. Furthermore, the proposed OLCA has been employed to solve three real-world engineering problems, such as the tension/compression spring problem, the gear train problem, and the three-bar truss problem. The results demonstrate the OLCA’s superiority and capability over other algorithms in solving engineering problems.
Today, intelligent optimization has become a science that few researchers have not used in dealing with problems in their field. Diversity and flexibility have made the use, efficiency, and usefulness of various nature-inspired optimization methods, such as evolutionary and meta-heuristic algorithms, more evident in such problems. This work first provides a comprehensive overview of all considerations governing various optimization problems with detailed corresponding categories. Then, the most comprehensive review and recent methods (during 1965–2022) are presented in evolution-based, swarm-based, physics-based, human-based, and hybrid-based categories. More than 320 new algorithms have been reviewed. All specifications including authors, year, abbreviation, inspired source, controls, and their application are considered in this regard. Statistical analyzes of papers and publishers, annually and for 57 years, along with their ranking, are also examined in detail. Among the key achievements of the paper include: the most number of algorithms with 47.71% (156 methods) have been from the swarm category, and most of them were published in the five years of 2021 (72, 22.02%), 2020 (39, 11.93%), 2022 (31, 9.48%), 2019 (26, 7.95%), and 2016 (21, 6.42%) respectively; the top five rankings of publishers of reviewed algorithms/papers were also: “Proceedings of the Congress” (33, 10.09%), “Applied Soft Computing” (19, 5.81%), “Expert Systems with Applications” (18, 5.51%), “Knowledge-Based Systems” (12, 3.67%), “Engineering Applications of Artificial Intelligence” (12, 3.67%), “Advances in Engineering Software” (12, 3.67%), " Neural Computing and Applications " (12, 3.67%), and " Information Sciences " (11, 3.36%). The paper's data is available at: https://github.com/ali-ece.
In a supply chain that consists of a supplier or manufacturer, a retailer, and a customer, the supplier regularly offers the retailer a pay in later facility in terms of S periods, while the retailer then gives their client a pay in later facility in terms of
N
periods to increase sales and decrease inventory. Offering trade credit benefits the seller’s sales and profits, but it also increases default risk. As a result, understanding the credit period is becoming widely acknowledged as a key tactic for boosting seller profitability. This study suggests an EOQ model in the perspective of retailer point of view for which: (a) both the supplier and the retailer supply up-stream pay in later facility; (b) downstream trade credit provided from the retailer to the buyer increases opportunity cost and default risk in addition to sales and profitability; (c) items that are degrading not only continue to degrade over time but also have an expiration date. We employed the well-known metaheuristic algorithm Grey Wolf Optimizer (GWO) to solve the optimisation problem because the objective function is high nonlinear nature. In addition, we have compared the results with some other metaheuristic algorithm. In order to highlight the problem and provide managerial advice, we conclude by using some numerical examples.
This paper describes an artificial chemistry featuring atoms and molecules moving and colliding in a continuous manner in a viscous fluid filling a 2D cellular space. Chemical reactions are mappings of discrete cellular configurations to parameterized actions on atoms. Actions allow atom creation and d estruction, bonding and unb onding to make a nd b reak molecules, orientation, type change, and propulsion. Actions are ea sily added in this extensible model. An example involving a complex "foraging" reaction is provided as a demonstration of the capabilities of the framework. The reaction rules can be evolved by a genetic algorithm to exhibit a desired set of reactions. A portion of the foraging reaction was evolved to demonstrate this.
In many complex optimization problems, the so-called stochastic optimization methods as genetic algorithms and simulated annealing have been applied successfully. However, these methods have drawbacks: simulated annealing is too sensitive to the choice of parameters and the canonical genetic algorithm is liable to suboptimal convergence. With the purpose of overcoming these drawbacks, a new stochastic optimization algorithm is introduced in this article. It is loosely inspired by nuclear collision reactions, particularly scattering (where an incident neutron is scattered by a collision with a target nucleus) and absorption (where the incident neutron is absorbed by the target nucleus). This algorithm was named "Particle Collision Algorithm" (PCA). PCA resembles in its structure simulated annealing. A basic difference is that, besides the number of iterations, it does not rely on user-defined parameters. First, the new algorithm is validated using three well known test functions. Then, PCA is applied to an engineering design optimization problem: a nuclear core design optimization. This problem consists in adjusting several reactor cell parameters, such as dimensions, enrichment and materials, in order to minimize the average peak-factor in a 3-enrichment-zone reactor, considering operational restrictions. Minimizing the average peak-factor is important for safety reasons, avoiding the risk of a fuel rod meltdown, and for economical reasons, so that the cycle length is maximized. The results are compared to those obtained in previous efforts using the canonical genetic algorithm and the niching genetic algorithm. PCA performs better than both methods, showing its potential for other applications.
This paper proposes a new heuristic for global optimization. The method utilizes an attraction-repulsion mechanism to move the sample points towards the optimality. The proposed scheme can be used either as a stand-alone approach or as an accompanying procedure for other methods. Some test results on nonlinear test functions in the category of ``minor to moderate difficulty'' are included. The ease of implementation and flexibility of the heuristic show the potential of this new approach.
Swarm intelligence is a research branch that models the population of interacting agents or swarms that are able to self-organize.
An ant colony, a flock of birds or an immune system is a typical example of a swarm system. Bees’ swarming around their hive
is another example of swarm intelligence. Artificial Bee Colony (ABC) Algorithm is an optimization algorithm based on the
intelligent behaviour of honey bee swarm. In this work, ABC algorithm is used for optimizing multivariable functions and the
results produced by ABC, Genetic Algorithm (GA), Particle Swarm Algorithm (PSO) and Particle Swarm Inspired Evolutionary Algorithm
(PS-EA) have been compared. The results showed that ABC outperforms the other algorithms.
Biogeography is the study of the geographical distribution of biological organisms. Mathematical equations that govern the distribution of organisms were first discovered and developed during the 1960s. The mindset of the engineer is that we can learn from nature. This motivates the application of biogeography to optimization problems. Just as the mathematics of biological genetics inspired the development of genetic algorithms (GAs), and the mathematics of biological neurons inspired the development of artificial neural networks, this paper considers the mathematics of biogeography as the basis for the development of a new field: biogeography-based optimization (BBO). We discuss natural biogeography and its mathematics, and then discuss how it can be used to solve optimization problems. We see that BBO has features in common with other biology-based optimization methods, such as GAs and particle swarm optimization (PSO). This makes BBO applicable to many of the same types of problems that GAs and PSO are used for, namely, high-dimension problems with multiple local optima. However, BBO also has some features that are unique among biology-based optimization methods. We demonstrate the performance of BBO on a set of 14 standard benchmarks and compare it with seven other biology-based optimization algorithms. We also demonstrate BBO on a real-world sensor selection problem for aircraft engine health estimation.
In this paper, we present a new algorithm of swarm intelligence, namely, Cat Swarm Optimization (CSO). CSO is generated by
observing the behaviors of cats, and composed of two sub-models, i.e., tracing mode and seeking mode, which model upon the
behaviors of cats. Experimental results using six test functions demonstrate that CSO has much better performance than Particle
Swarm Optimization (PSO).
The saplings sowing and growing up algorithm (SGA) was inspired by a natural events – evolution of growing up of trees. This
algorithm contains two phases: Sowing Phase and Growing up Phase. In this paper, the theoretical foundations of SGA were determined.
SGA is defined as a computational model, and it was depicted that there are a collection of Turing Machines for simulating
SGA.
In this paper a new genetic algorithm called the Breeder Genetic Algorithm (BGA) is introduced. The BGA is based on artificial selection similar to that used by human breeders. A predictive model for the BGA is presented that is derived from quantitative genetics. The model is used to predict the behavior of the BGA for simple test functions. Different mutation schemes are compared by computing the expected progress to the solution. The numerical performance of the BGA is demonstrated on a test suite of multimodal functions. The number of function evaluations needed to locate the optimum scales only as n ln(n) where n is the number of parameters. Results up to n = 1000 are reported.
A different crossover operator, uniform crossover, is presented. It is compared theoretically and empirically with one-point and two-point crossover and is shown to be superior in most cases.
We are given a large database of customer transactions. Each transaction consists of items purchased by a customer in a visit. We present an efficient algorithm that generates all significant association rules between items in the database. The algorithm incorporates buffer management and novel estimation and pruning techniques. We also present results of applying this algorithm to sales data obtained from a large retailing company, which shows the effectiveness of the algorithm.
Nature-inspired algorithms are among the most powerful algorithms for optimization. This paper intends to provide a detailed description of a new Firefly Algorithm (FA) for multimodal optimization applications. We will compare the proposed firefly algorithm with other metaheuristic algorithms such as particle swarm optimization (PSO). Simulations and results indicate that the proposed firefly algorithm is superior to existing metaheuristic algorithms. Finally we will discuss its applications and implications for further research.
An analogy with the way ant colonies function has suggested the definition of a new computational paradigm, which we call ant system (AS). We propose it as a viable new approach to stochastic combinatorial optimization. The main characteristics of this model are positive feedback, distributed computation, and the use of a constructive greedy heuristic. Positive feedback accounts for rapid discovery of good solutions, distributed computation avoids premature convergence, and the greedy heuristic helps find acceptable solutions in the early stages of the search process. We apply the proposed methodology to the classical traveling salesman problem (TSP), and report simulation results. We also discuss parameter selection and the early setups of the model, and compare it with tabu search and simulated annealing using TSP. To demonstrate the robustness of the approach, we show how the ant system (AS) can be applied to other optimization problems like the asymmetric traveling salesman, the quadratic assignment and the job-shop scheduling. Finally we discuss the salient characteristics-global data structure revision, distributed communication and probabilistic transitions of the AS.
The clonal selection principle is used to explain the basic
features of an adaptive immune response to an antigenic stimulus. It
establishes the idea that only those cells that recognize the antigens
(Ag's) are selected to proliferate. The selected cells are subject to an
affinity maturation process, which improves their affinity to the
selective Ag's. This paper proposes a computational implementation of
the clonal selection principle that explicitly takes into account the
affinity maturation of the immune response. The general algorithm, named
CLONALG, is derived primarily to perform machine learning and pattern
recognition tasks, and then it is adapted to solve optimization
problems, emphasizing multimodal and combinatorial optimization. Two
versions of the algorithm are derived, their computational cost per
iteration is presented, and a sensitivity analysis in relation to the
user-defined parameters is given. CLONALG is also contrasted with
evolutionary algorithms. Several benchmark problems are considered to
evaluate the performance of CLONALG and it is also compared to a niching
method for multimodal function optimization
In this paper a new genetic algorithm called the Breeder Genetic Algorithm (BGA) is introduced. The BGA is based on artificial selection similar to that used by human breeders. A predictive model for the BGA is presented which is derived from quantitative genetics. The model is used to predict the behavior of the BGA for simple test functions. Different mutation schemes are compared by computing the expected progress to the solution. The numerical performance of the BGA is demonstrated on a test suite of multimodal functions. The number of function evaluations needed to locate the optimum scales only as n ln(n) where n is the number of parameters. Results up to n = 1000 are reported. 1 Introduction In (Muhlenbein, Gorges-Schleuter & Kramer 1988), (Muhlenbein, 1991), (Muhlenbein, 1992b) the parallel genetic algorithm (PGA) was successfully applied to combinatorical problems. Continuous parameter optimization has been described in (Muhlenbein, Schomisch & Born, 1991). We have ...
In this study a relatively new member of swarm intelligence family that is named as "artificial bee colony" is explained in detail. Actually, different names were used in the literature for the algorithms inspired from natural honey bees. Here we prefer to use the name "artificial bee colony" to reflect population characteristic of the algorithm. A very detailed literature review along with a categorization is presented in this study. All accessible previous work on bee based optimization algorithms is tried to be reviewed. Most of the work in the literature is carried out in last two years and researchers mainly concentrated on continuous optimization and TSP problems. Previous work has presented that bee inspired algorithms have a very promising potential for modelling and solving complex optimization problems. But there is still a long way to go in order to fully utilise the potential of bee inspired algorithms. Such an attempt is also made in this study to present performance of a bee inspired algorithm, "artificial bee colony" on a NP-hard problem which is known as generalised assignment problem. The proposed bee algorithm is found very effective in solving small to medium sized generalized assignment problems. Actually, the proposed algorithm easily found all optimal solutions where the compared 12 algorithms were not able to find for most of the cases. Our research is still under progress and we are hoping to find effective solutions for large size and tightly constrained generalised assignment problems. These problems are over complex, therefore their solution can be considered as a very good indicator for the potential of the nature inspired algorithms including "artificial bee colony".
This paper presents a novel optimization algorithm for minimizing weight of skeletal structures. The algorithm-denoted as MPISA (Multi Point Improved Simulated Annealing)-utilizes a multi-level simulated annealing scheme where different candidate designs are compared simultaneously. This is done in purpose to increase computational efficiency and to minimize the number of exact structural analyses. MPISA is tested in three complicated design problems of skeletal structures: (i) sizing optimization of a planar bar truss under five independent loading conditions including 200 design variables; (ii) sizing-configuration optimization of a cantilevered bar truss including 81 design variables; (iii) sizing-configuration optimization of a frame structure including 84 design variables. The new algorithm is compared to another multilevel simulated annealing algorithm and to gradient based optimizers recently presented in literature or included in commercial software. Numerical results demonstrate the very high efficiency of MPISA which achieved weight savings ranging between 250 and 350 kg in all test problems.
This paper proposes a novel particle swarm optimization (PSO) algorithm, chaotically encoded particle swarm optimization algorithm (CENPSOA), based on the notion of chaos numbers that have been recently proposed for a novel meaning to numbers. In this paper, various chaos arithmetic and evaluation measures that can be used in CENPSOA have been described. Furthermore, CENPSOA has been designed to be effectively utilized in data mining applications.
We propose a novel metaheuristic search for global optimization inspired by the behavior of a monkey climbing trees looking for food. The tree branches are represented as perturbations between two neighboring feasible solutions of the considered global optimization problem. The monkey mark and update these branches leading to good solutions as it climbs up and down the tree. A wide selection of perturbations can be applied based on other metaheuristic methods for global optimization. We show that Monkey Search is competitive compared to the other metaheuristic methods for optimizing Lennard-Jones and Morse clusters, and for simulating protein molecules based on a geometric model for protein folding.
Classication is a supervised learning method that induces a classication model from a database and is one of the most commonly applied data mining task. The frequently employed techniques are decision tree or neural network-based classication algorithms. This work presents an ecient genetic algorithm (GA) for classication rule mining technique that discovers comprehensible IF-THEN rules using a generalized uniform population method and a uniform operator inspired from the uniform population method. Initial population is generated by methodically eliminating the randomness by generalized uniform population method. In the subsequence generations, genetic diversity is ensured and premature convergence is prevented by the uniform operator. From the experimental results, it was observed that, this method handled the problems of GAs in the task of classication and guaranteed to get rid of any local solution and rapidly found comprehensible rules.
This paper proposes an algorithm—multipoint Tabu Search (MPTS) based on POP—which has several advantages for solving combinatorial optimization problems. The proposed algorithm is applied to some problems of traveling salesmen, which are typical combinatorial optimization problems, in order to verify the performance of the proposed algorithm. The simulation results indicate that the proposed method has higher optimality than the conventional Tabu Search (TS). © 2006 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
In this paper, a genetic algorithm (GA) is proposed as a search strategy for not only positive but also negative quantitative
association rule (AR) mining within databases. Contrary to the methods used as usual, ARs are directly mined without generating
frequent itemsets. The proposed GA performs a database-independent approach that does not rely upon the minimum support and
the minimum confidence thresholds that are hard to determine for each database. Instead of randomly generated initial population,
uniform population that forces the initial population to be not far away from the solutions and distributes it in the feasible
region uniformly is used. An adaptive mutation probability, a new operator called uniform operator that ensures the genetic
diversity, and an efficient adjusted fitness function are used for mining all interesting ARs from the last population in
only single run of GA. The efficiency of the proposed GA is validated upon synthetic and real databases.
This paper proposes an algorithm for optimization inspired by the imperialistic competition. Like other evolutionary ones, the proposed algorithm starts with an initial population. Population individuals called country are in two types: colonies and imperialists that all together form some empires. Imperialistic competition among these empires forms the basis of the proposed evolutionary algorithm. During this competition, weak empires collapse and powerful ones take possession of their colonies. Imperialistic competition hopefully converges to a state in which there exist only one empire and its colonies are in the same position and have the same cost as the imperialist. Applying the proposed algorithm to some of benchmark cost functions, shows its ability in dealing with different types of optimization problems.
We encounter optimization problems in our daily lives and in various research domains. Some of them are so hard that we can, at best, approximate the best solutions with (meta-) heuristic methods. However, the huge number of optimization problems and the small number of generally acknowledged methods mean that more metaheuristics are needed to fill the gap. We propose a new metaheuristic, called chemical reaction optimization (CRO), to solve optimization problems. It mimics the interactions of molecules in a chemical reaction to reach a low energy stable state. We tested the performance of CRO with three nondeterministic polynomial-time hard combinatorial optimization problems. Two of them were traditional benchmark problems and the other was a real-world problem. Simulation results showed that CRO is very competitive with the few existing successful metaheuristics, having outperformed them in some cases, and CRO achieved the best performance in the real-world problem. Moreover, with the No-Free-Lunch theorem, CRO must have equal performance as the others on average, but it can outperform all other metaheuristics when matched to the right problem type. Therefore, it provides a new approach for solving optimization problems. CRO may potentially solve those problems which may not be solvable with the few generally acknowledged approaches.
Artificial bee colony (ABC) is the one of the newest nature inspired heuristics for optimization problem. Like the chaos in real bee colony behavior, this paper proposes new ABC algorithms that use chaotic maps for parameter adaptation in order to improve the convergence characteristics and to prevent the ABC to get stuck on local solutions. This has been done by using of chaotic number generators each time a random number is needed by the classical ABC algorithm. Seven new chaotic ABC algorithms have been proposed and different chaotic maps have been analyzed in the benchmark functions. It has been detected that coupling emergent results in different areas, like those of ABC and complex dynamics, can improve the quality of results in some optimization problems. It has been also shown that, the proposed methods have somewhat increased the solution quality, that is in some cases they improved the global searching capability by escaping the local solutions.
This paper introduces a novel numerical stochastic optimization algorithm inspired from colonizing weeds. Weeds are plants whose vigorous, invasive habits of growth pose a serious threat to desirable, cultivated plants making them a threat for agriculture. Weeds have shown to be very robust and adaptive to change in environment. Thus, capturing their properties would lead to a powerful optimization algorithm. It is tried to mimic robustness, adaptation and randomness of colonizing weeds in a simple but effective optimizing algorithm designated as Invasive Weed Optimization (IWO). The feasibility, the efficiency and the effectiveness of IWO are tested in details through a set of benchmark multi-dimensional functions, of which global and local minima are known. The reported results are compared with other recent evolutionary-based algorithms: genetic algorithms, memetic algorithms, particle swarm optimization, and shuffled frog leaping. The results are also compared with different versions of simulated annealing — a generic probabilistic meta-algorithm for the global optimization problem — which are simplex simulated annealing, and direct search simulated annealing. Additionally, IWO is employed for finding a solution for an engineering problem, which is optimization and tuning of a robust controller. The experimental results suggest that results from IWO are better than results from other methods. In conclusion, the performance of IWO has a reasonable performance for all the test functions.
Most engineering optimization algorithms are based on numerical linear and nonlinear programming methods that require substantial gradient information and usually seek to improve the solution in the neighborhood of a starting point. These algorithms, however, reveal a limited approach to complicated real-world optimization problems. If there is more than one local optimum in the problem, the result may depend on the selection of an initial point, and the obtained optimal solution may not necessarily be the global optimum. This paper describes a new harmony search (HS) meta-heuristic algorithm-based approach for engineering optimization problems with continuous design variables. This recently developed HS algorithm is conceptualized using the musical process of searching for a perfect state of harmony. It uses a stochastic random search instead of a gradient search so that derivative information is unnecessary. Various engineering optimization problems, including mathematical function minimization and structural engineering optimization problems, are presented to demonstrate the effectiveness and robustness of the HS algorithm. The results indicate that the proposed approach is a powerful search and optimization technique that may yield better solutions to engineering problems than those obtained using current algorithms.
This paper proposes new particle swarm optimization (PSO) methods that use chaotic maps for parameter adaptation. This has been done by using of chaotic number generators each time a random number is needed by the classical PSO algorithm. Twelve chaos-embedded PSO methods have been proposed and eight chaotic maps have been analyzed in the benchmark functions. It has been detected that coupling emergent results in different areas, like those of PSO and complex dynamics, can improve the quality of results in some optimization problems. It has been also shown that, some of the proposed methods have somewhat increased the solution quality, that is in some cases they improved the global searching capability by escaping the local solutions.
In this paper, classification rule mining which is one of the most studied tasks in data mining community has been modeled as a multi-objective optimization problem with predictive accuracy and comprehensibility objectives. A multi-objective chaotic particle swarm optimization (PSO) method has been introduced as a search strategy to mine classification rules within datasets. The used extension to PSO uses similarity measure for neighborhood and far-neighborhood search to store the global best particles found in multi-objective manner. For the bi-objective problem of rule mining of high accuracy/comprehensibility, the multi-objective approach is intended to allow the PSO algorithm to return an approximation to the upper accuracy/comprehensibility border, containing solutions that are spread across the border. The experimental results show the efficiency of the algorithm.
In this study, a classification model including fuzzy system, artificial immune system (AIS), and boosting is proposed. The
model is mainly focused on the clonal selection principle of biological immune system and evolves a population of antibodies,
where each antibody represents the antecedent of a fuzzy classification rule while each antigen represents an instance. The
fuzzy classification rules are mined in an incremental fashion, in that the AIS optimizes one rule at a time. The boosting
mechanism that is used to increase the accuracy rates of the rules reduces the weight of training instances that are correctly
classified by the new rule. Whenever AIS mines a rule, this rule is added to the mined rule list and mining of next rule focuses
on rules that account for the currently uncovered or misclassified instances. The results obtained by proposed approach are
analyzed with respect to predictive accuracy and simplicity and compared with C4.5Rules.
Saplings Growing up Algorithm (SGA) is a novel computational intelligence method inspired by sowing and growing up of saplings.
This method contains two phases: Sowing Phase and Growing up Phase. Uniformed sowing sampling is aim to scatter evenly in
the feasible solution space. Growing up phase contains three operators: mating, branching, and vaccinating operator. In this study thinking capability of SGA has been defined and it has been demonstrated that sapling population generated
initially has diversity. The similarity of population concludes the interaction of saplings and at consequent, they will be
similar. Furthermore, the operators used in the algorithm uses similarity and hence the population has the convergence property.
Ant colony optimization (ACO) is relatively new computational intelligence paradigm and provides an effective mechanism for
conducting a global search. This work proposes a novel classification rule mining algorithm integrating ACO for search strategy
and fuzzy set for representation of the rule terms to give the system flexibility to cope with continuous values and uncertainties
typically found in real-world applications and improve the comprehensibility of the rules. The algorithm uses a strategy that
is different from ‘divide-and-conquer’ and ‘separate-and-conquer’ approaches used by decision trees and lists respectively;
and simulates the ants’ searching different food sources by using attribute-instance weighting and an effective pheromone
update strategy for mining accurate and comprehensible rules. Obtained results from several real-world data sets are analyzed
with respect to both predictive accuracy and simplicity and compared with C4.5Rules algorithm.
Two extensive analyzes on RnaPredict, an evolutionary algorithm for RNA folding, are presented here. The first study evaluates the performance of individual nearest neighbor (INN) and individual nearest neighbor-hydrogen bond (INN-HB), two stacking-energy thermodynamic models; the criteria for comparison is the correlation between the prediction accuracy and the free energy of predicted structures for 9 RNA sequences. Despite some variance, a trend between lower free energies and increases in true positive base pairs is apparent. In general, this correlation decreases as the sequence length increases. The second study compares the performance of RnaPredict against the mfold dynamic programming algorithm (DPA) on the same sequences in terms of specificity and sensitivity. The results indicate that RnaPredict has comparable performance to mfold on sub-optimal structures, and outperforms mfold's minimum free energy structures
Harmony Search (HS) is one of the newest and the easiest to code music inspired heuristics for optimization problems. Like the use of chaos in adjusting note parameters such as pitch, dynamic, rhythm, duration, tempo, instrument selection, attack time, etc. in real music and in sound synthesis and timbre construction, this paper proposes new HS algorithms that use chaotic maps for parameter adaptation in order to improve the convergence characteristics and to prevent the HS to get stuck on local solutions. This has been done by using of chaotic number generators each time a random number is needed by the classical HS algorithm. Seven new chaotic HS algorithms have been proposed and different chaotic maps have been analyzed in the benchmark functions. It has been detected that coupling emergent results in different areas, like those of HS and complex dynamics, can improve the quality of results in some optimization problems. It has been also shown that, some of the proposed methods have somewhat increased the solution quality, that is in some cases they improved the global searching capability by escaping the local solutions.
Nature is the principal source for proposing new optimization methods such as genetic algorithms (GA) and simulated annealing (SA) methods. All traditional evolutionary algorithms are heuristic population-based search procedures that incorporate random variation and selection. The main contribution of this study is that it proposes a novel optimization method that relies on one of the theories of the evolution of the universe; namely, the Big Bang and Big Crunch Theory. In the Big Bang phase, energy dissipation produces disorder and randomness is the main feature of this phase; whereas, in the Big Crunch phase, randomly distributed particles are drawn into an order. Inspired by this theory, an optimization algorithm is constructed, which will be called the Big Bang-Big Crunch (BB-BC) method that generates random points in the Big Bang phase and shrinks those points to a single representative point via a center of mass or minimal cost approach in the Big Crunch phase. It is shown that the performance of the new (BB-BC) method demonstrates superiority over an improved and enhanced genetic search algorithm also developed by the authors of this study, and outperforms the classical genetic algorithm (GA) for many benchmark test functions.
This paper proposes a novel particle swarm optimization algorithm, rough particle swarm optimization algorithm (RPSOA), based
on the notion of rough patterns that use rough values defined with upper and lower intervals that represent a range or set
of values. In this paper, various operators and evaluation measures that can be used in RPSOA have been described and efficiently
utilized in data mining applications, especially in automatic mining of numeric association rules which is a hard problem.
David Goldberg's Genetic Algorithms in Search, Optimization and Machine Learning is by far the bestselling introduction to genetic algorithms. Goldberg is one of the preeminent researchers in the field--he has published over 100 research articles on genetic algorithms and is a student of John Holland, the father of genetic algorithms--and his deep understanding of the material shines through. The book contains a complete listing of a simple genetic algorithm in Pascal, which C programmers can easily understand. The book covers all of the important topics in the field, including crossover, mutation, classifier systems, and fitness scaling, giving a novice with a computer science background enough information to implement a genetic algorithm and describe genetic algorithms to a friend.
We study the computational complexity of two popular problems in multiple sequence alignment: multiple alignment with SP-score and multiple tree alignment. It is shown that the first problem is NP-complete and the second is MAX SNP-hard. The complexity of tree alignment with a given phylogeny is also considered.
eberhart @ engr.iupui.edu A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described, and applications, including nonlinear function optimization and neural network training, are proposed. The relationships between particle swarm optimization and both artificial life and genetic algorithms are described, 1
We explain the biology and physics underlying the chemotactic
(foraging) behavior of E. coli bacteria. We explain a variety of
bacterial swarming and social foraging behaviors and discuss the control
system on the E. coli that dictates how foraging should proceed. Next, a
computer program that emulates the distributed optimization process
represented by the activity of social bacterial foraging is presented.
To illustrate its operation, we apply it to a simple multiple-extremum
function minimization problem and briefly discuss its relationship to
some existing optimization algorithms. The article closes with a brief
discussion on the potential uses of biomimicry of social foraging to
develop adaptive controllers and cooperative control strategies for
autonomous vehicles. For this, we provide some basic ideas and invite
the reader to explore the concepts further
Artificial bee colony algorithm and its application to generalized assignment problem Chapter 8 of swarm intelligence: Focus on ant and particle swarm optimization (pp. 532). Itech Education and Publishing An electromagnetism-like mechanism for global optimization
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Engineering thermodynamics
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Nag, P. K. (2008). Engineering thermodynamics (4th ed.). McGraw-Hill, pp. 9-10.