Conference Paper

A New Optimizer Using Particle Swarm Theory

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Abstract

The optimization of nonlinear functions using particle swarm methodology is described. Implementations of two paradigms are discussed and compared, including a recently developed locally oriented paradigm. Benchmark testing of both paradigms is described, and applications, including neural network training and robot task learning, are proposed. Relationships between particle swarm optimization and both artificial life and evolutionary computation are reviewed

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... First I handpicked feasible cognitive concepts to design some RL-friendly metaheuristics. The implemented concepts are hierarchical thinking [38], actor-observer effect [39], and conflictmonitored avoidance learning behavior [37]. Then I used the outcome heuristics to suggest new jumping states and actions in each episode update of Temporal Difference (TD) Learning variants. ...
... Nature-inspired meta-heuristics optimization methods inspire or mimic the behavior of nature, human cognition and biology to desire a meta-heuristic suitable for problem of interest. To improve my insight on the way Temporal Difference (TD) variants optimize the best action in RL environment, I design metaheuristics to apply fundamental cognitive laws (like Modularity, Hierarchy, organization, cognitive biases, Conflict Modulating mechanisms, self-awareness, uncertainty principle... [36][37][38][39][40][41]) for developing state/policy randomization strategies between each TD iteration to control exploration and exploitation more powerfully depending the environment of interest. The results help me find out what kind of environment with what scale and constraints need what scheme of exploration with what preferred nature-inspired plan. ...
... Among the most frequently used algorithms one can mention Genetic Agorithms (GA), Particle Swarm Optimization, Bat Search Algorithms [37,38,39]. ...
Article
Optimization of the best policy has a long history in Reinforcement Learning (RL). However due to No Free Lunch theorem, no optimizer guaranties to find the optimal solution for all possible problems. Problems like Low Exploration/Exploitation, non-Markovian behavior and wideness of policy/state space can affect a lot on the speed and performance of sought actions. With this project, I inspire brain's cognitive mechanisms/laws/biases to design meta-heuristics [1,3,11] helpful for better RL policy optimization. First I handpicked feasible cognitive concepts to design some RL-friendly metaheuristics. The implemented concepts are hierarchical thinking [38], actor-observer effect [39], and conflict-monitored avoidance learning behavior [37]. Then I used the outcome heuristics to suggest new jumping states and actions in each episode update of Temporal Difference (TD) Learning variants. The three variants are Q-learning, SARSA, and Expected-SARSA. Having evaluated the hybrid algorithms in Windy Grid problem, I compared suitability of heuristics-TD versus randomWalker-TD and TD itself, tested out advantages of cognitive-inspired metaheuristics over non-cognitive variants, and dived deeper into comparing TD-variants by analyzing their behavior with and without exposure to metaheuristics. Without meta-heuristics, in the best case it took 850 episodes to reach the goal, while with metaheuristics (as a controlling algorithm between exploration and exploitation), it took 500 episodes to reach the goal.
... Many algorithms have been described in the existing literature to address real-world problems, including PSO [20], ant colony optimization (ACO) [21], the harmony search algorithm (HSA) [22], and the artificial bee colony (ABC) approach [23]. PSO mimics the social behavior of fish or birds' movements. ...
... The most popular swarm intelligent optimization algorithm is PSO, introduced by Eberhart and Kennedy [20]. This approach mimics the social behavior of fish or birds. ...
... The movements of the proposed C 1 and C 2 coefficients are shown in Fig. 7. The velocity is defined as [20]: where w is the inertia, therefore, the new position for the swarms is defined as [20]: ...
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The World Health Organization has declared the COVID-19 pandemic, with most countries being affected by this virus both socially and economically. It thus became necessary to develop solutions to help monitor and control disease spread by controlling medical workers' movements and warning them against approaching infected individuals in isolation rooms. This paper introduces a control system that uses improved particle swarm optimization (PSO), and artificial neural network (ANN) approaches to achieve social distancing. The distance between medical workers carrying mobile nodes and the beacon node (isolation room) was determined using the ZigBee wireless protocol's received signal strength indicator (RSSI). Two path loss models were developed to determine the distance from patients with COVID-19: the first is a log-normal shading model (LNSM), and the second is a polynomial function (POL). The coefficient values of the POL model were controlled based on PSO to improve model performance. A random-nonlinear time variation controller-PSO (RNT-PSO) approach was developed to avoid the local minima of the conventional PSO. As a result, social distancing for COVID-19 can be accurately determined. The measured RSSI and the distance were used as ANN inputs, while three control signals (alarming, warning, and closing) were used as ANN outputs. The results revealed that the hybrid model between POL and RNT-PSO, called RNT-PSO-POL, improved the system's performance by reducing the mean absolute error of distance to 1.433 m, compared to 1.777 m for the LNSM. The results show that the ANN achieves robust performance in terms of mean squared error.
... Another type of algorithm is the intelligent algorithm: it is an algorithm that people model by nature-inspired or human mind to imitate solving problems [16]. Typical algorithms are particle swarm optimization (PSO) [17], ant colony optimization (ACO) [18], neural network (NN) [19], and other algorithms. Intelligent algorithms play an effective role in solving complex dynamic environments, but there are common problems such as slow computation speed, poor stability, poor real-time performance, and easy to fall into local optimality. ...
... When the MR tracks Computational Intelligence and Neuroscience the path, the shorter the distance traveled, the shorter time required to complete the task, and the less energy consumed. erefore, this study defines the path length of the MR from the starting position to the goal position as shown in equation (17). ...
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The path-planning approach plays an important role in determining how long the mobile robots can travel. To solve the path-planning problem of mobile robots in an unknown environment, a potential and dynamic Q-learning (PDQL) approach is proposed, which combines Q-learning with the artificial potential field and dynamic reward function to generate a feasible path. The proposed algorithm has a significant improvement in computing time and convergence speed compared to its classical counterpart. Experiments undertaken on simulated maps confirm that the PDQL when used for the path-planning problem of mobile robots in an unknown environment outperforms the state-of-the-art algorithms with respect to two metrics: path length and turning angle. The simulation results show the effectiveness and practicality of the proposal for mobile robot path planning.
... In 1991, Colorni et al. [4] proposed the ant colony optimization (ACO), which is inspired from the colony behavior of ants' foraging. In 1995, Eberhart and Keendy et al. [5] proposed a particle swarm optimization (PSO) based on the foraging behavior of birds. In 1997, Storn and Price [6] proposed differential evolution (DE) based on GA. ...
... e EMFO gains poor convergence speed on the other 6 functions (F5, F7, F8, F12, F14, and F15), which could be because the EMFO needs a longer time to jump out of the local optimum. In addition, the MFO outperforms the other 6 algorithms on 5 functions (F1-F3, F8, and F12), and the CMFO gains the best (1) Begin (2) Initialize the parameters; (3) Initialize uniformly random population; (4) gen � 1; (5) while not meet termination condition (6) if mod(gen, 10) � � 0 (7) Update the number of flames by using Equation (9) or Equation (10); (8) end if (9) Calculate the fitness values of moths; (10) if gen � 1 (11) F � sort(M); OF � sort(OM); (12) else ...
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How to accurately and efficiently extract photovoltaic (PV) model parameters is the primary problem of photovoltaic system optimization. To accurately and efficiently extract the parameters of PV models, an enhanced moth-flame optimization (EMFO) with multiple flame guidance mechanism is proposed in this study. In EMFO, an adaptive flame number updating mechanism is used to adaptively control the flame number, which enhances the local and global exploration capabilities of MFO. Meanwhile, a multiple flame guidance mechanism is designed for the full use of the position information of flames, which enhances the global diversity of the population. The EMFO is evaluated with other variants of the MFO on 25 benchmark functions of CEC2005, 28 functions of CEC2017, and 5 photovoltaic model parameter extraction problems. Experimental results show that the EMFO has obtained a better performance than other compared algorithms, which proves the effectiveness of the proposed EMFO. The method proposed in this study provides MFO researchers with ideas for adaptive research and making full use of flame population information.
... The famous evolutionary-based algorithms enclose genetic algorithm (GA) (Holland 1992) and differential evolutionary (DE) (Price et al. 2006) which are developed based on the idea of Darwin's evolution theory (Darwin 1859), while swarm intelligence-related algorithms are inspired by the conduct of various physical phenomena and biological species. These include particle swarm optimization (PSO) (Eberhart and Kennedy 1995), artificial prey-predator (APP) (Bohat and Arya 2017), gravitational search algorithm (GSA) (Rashedi et al. 2009), cuckoo search algorithm (CS) (Yang and Deb 2009), grey wolf optimization (GWO) (Mirjalili et al. 2014), etc. The PSO algorithm is motivated by fish schooling and bird flocking, GSA is based on Newton's second law of motion, and CS is inspired by the brooding behaviors of cuckoos. ...
... The effectiveness of the proposed S-GWO-FH model is confirmed by comparing its performance with numerous attractive NIA's including PSO (Eberhart and Kennedy 1995), In addition to this, the outcomes of the S-GWO-FH model are also examined with some conventional and state-of-theart FSR techniques namely bicubic, NEDI (Li and Orchard 2001), Eign (Wang and Tang 2005), and LSR (Ma et al. 2010). ...
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In recent years, face hallucination (FH) techniques are developed to generate the high-resolution (HR) version of the captured blurry low-resolution face images. The popular FH techniques transform the face hallucination problem as a least square representation (LSR) formulation. The performance of these FH techniques entirely depends on how optimally the LSR problem is minimized. Hence, in this paper, a new FH model using a sparsity-based grey wolf optimization algorithm (named S-GWO-FH) is developed which optimizes the LSR problem more efficiently. The concept of sparsity with GWO helps the proposed FH model in ignoring the dislike training images; consequently, reconstructed images have better personal characteristics. It also helps in minimizing computational overhead as it reduces the population size. Moreover, a domain-specific prior is incorporated to initialize the positions of the grey wolves, which helps the GWO algorithm to converse with the more appropriate solution. Several state-of-the-art nature-inspired optimization algorithms and conventional super-resolution techniques are considered for performance comparison. The experiments results show that the proposed S-GWO-FH model is superior to competitive algorithms in terms of reconstruction capability as well as computation time.
... e load demand and the DGs including PV and WT DGs are varied across a day. Traditional algorithms consisting of genetic algorithm (GA) [36] and particle swarm optimization (PSO) [37] and a new efficient algorithm which is salp swarm algorithm (SSA) [38,39] are used to solve the problem, and their performances are compared before and after the BESS installation. e IEEE 33-and 69bus distribution systems with high DG penetration levels are tested to compare the system cost and performance enhancement after the BESS installation. ...
... PSO is a population-based stochastic optimization algorithm proposed by Eberhart and Kennedy [37]. It is one of the most wellknown and popular optimization algorithms due to its high efficiency in obtaining a feasible solution and its quickness to provide the solution. ...
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This work proposes an optimal location and sizing of battery energy storage system (BESS) installation for performance improvement of distribution systems with high distributed generation (DG) penetration level where the DGs comprise photovoltaics (PV) and wind turbines (WT). The installation of the BESS can reduce costs incurred in the systems, alleviate reverse power flow when the systems are in the high DG penetration level, and also achieve peak shaving during high demand. To find the optimal location and sizing of the BESS, three optimization algorithms, genetic algorithm (GA), particle swarm optimization (PSO), and salp swarm algorithm (SSA), are applied, and their performances are compared. The considered objective function is the system costs consisting of transmission loss cost, peak power cost, and voltage deviation cost. The system performance improvement is compared in terms of transmission loss, peak demand, and voltage regulation reductions. IEEE 33- and 69-bus distribution systems with high DG penetration are tested to investigate the performance improvement of the BESS installation. The results found that the installation of the BESS could successfully decrease system cost, improve voltage profile, reduce power losses, alleviate reverse power flow, and achieve peak shaving where PSO and SSA are found to be the best competitive algorithms. So, the proposed method can be further applied to find the optimal location and sizing of the BESS to improve the performance of practical systems in the future.
... Swarm Influenced by representative solutions Particle Swarm Optimization (PSO) [19] 1995 intelligence Creation and Stimergy Ant Colony optimization (ACO) [20] 1999 Creation-Combination Harmony Search Algorithm (HS) [21] 2001 Influenced by representative solutions Artificial Bee Colony (ABC) [22] 2007 Influenced by the entire population Central Force Optimization (CFO) [23] 2007 Creation-Combination Biogeography-based optimization (BBO) [24] 2008 Influenced by representative solutions Cuckoo Search (CS) [25] 2009 Influenced by neighborhoods Bacterial Foraging Optimization (BFO) [26] 2009 Influenced by the entire population Gravitational Search Algorithm (GSA) [27] 2009 Influenced by the entire population Firefly Optimizer (FFO) [28] 2010 Influenced by representative solutions Teaching-Learning-Based Optimizer (TLBO) [29] 2011 Influenced by representative solutions Fruit Fly Optimization (FFO) [30] 2012 Influenced by representative solutions Krill Herd (KH) [31] 2012 Influenced by representative solutions Grey Wolf Optimizer (GWO) [32] 2014 Influenced by representative solutions Harris Hawks Optimizer (HHO) [33] 2019 Influenced by representative solutions Henry Gas Solubility Optimization (HGSO) [34] 2019 Influenced by representative solutions Slime mold algorithm (SMA) [35] 2020 Influenced by mating behavior of snakes Snake Optimizer [36] 2022 ...
... The second category is Swarm-Intelligence (SI)-based algorithms, which are inspired from the social behavior of swarms, birds, insects, fish, and animals. The top three most popular examples of SI algorithms are Particle Swarm Optimization (PSO) by [19], Ant Colony Optimization (ACO) by [20], and Artificial Bee Colony (ABC) Algorithm by [62]. Some other SI-based algorithms that have their place in the literature regardless of their performance and originality include the Cuckoo Search Algorithm (CS) by [25], Firefly Algorithm (FA) by [63], COOT bird [64], Krill Herd (KH) by [31], Cat Swarm Optimization (CSO) by [65], Bat Algorithm (BA) by [66], Symbiotic Organisms Search (SOS) [67], Grey Wolf Optimizer (GWO) by [32], Moth-Flame Optimization (MFO) Algorithm checked by [68,69], Virus Colony Search (VCS) [70], Whale Optimization Algorithm (WOA)checked by [71,72], Grasshopper Optimization Algorithm (GOA) by [73], Salp Swarm Algorithm by [74,75], Crow Search Algorithm (CSA) reviewed by [76], Symbiotic Organisms Search (SOS) by [77], Reptile Search Algorithm (RSA) by [78], Butterfly Optimization Algorithm (BOA) by [79], Remora Optimization Algorithm (ROA) [80], Wild Horse Optimizer (WHO) [81], Seagull Optimization Algorithm (SOA) by [82], and Ant Lion Optimizer (ALO) reviewed by [83]. ...
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The Harris hawk optimizer is a recent population-based metaheuristics algorithm that simulates the hunting behavior of hawks. This swarm-based optimizer performs the optimization procedure using a novel way of exploration and exploitation and the multiphases of search. In this review research, we focused on the applications and developments of the recent well-established robust optimizer Harris hawk optimizer (HHO) as one of the most popular swarm-based techniques of 2020. Moreover, several experiments were carried out to prove the powerfulness and effectivness of HHO compared with nine other state-of-art algorithms using Congress on Evolutionary Computation (CEC2005) and CEC2017. The literature review paper includes deep insight about possible future directions and possible ideas worth investigations regarding the new variants of the HHO algorithm and its widespread applications.
... Particle swarm optimization (PSO) algorithm is a stochastic optimization method based on the Swarm Intelligence of a group of animals such as birds and fish developed by [27,28]. A point in the search space (i.e., a possible solution) is called a particle in the PSO algorithm. ...
... Based on Equations (22,23), the algorithm updates the position of every particle based on its current position, its best position in the past, and the best particle in the swarm. [28,29]. ...
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In developed economies, taxes based on residential property prices make a significant contribution to the sustainable income of the city managers. Therefore, estimating the price of residential properties is very important for economic purposes. Estimating the price of residential properties is a complex nonlinear, and multivariate problem. In this study, a hybrid method of support vector machine (SVM), genetic algorithm (GA) and particle swarm optimization (PSO) was used to estimate the price of residential properties. The support vector machine has been proven to be a powerful and robust algorithm for regression and classification. However, selecting the most appropriate hyper-parameters of this algorithm is a significant problem for its implementation. For hybrid SVR algorithms with PSO and GA, the mean absolute error is respectively 10.13% and 10.14%, based on the results of this study.
... Over the last few decades, various metaheuristic optimization methods are proposed like genetic algorithm (GA) (Holland 1992;Goldberg 2006), particle swarm optimization (PSO) (Eberhart and Kennedy 1995), differential evolution (DE) (Storn and Price 1997), ant colony optimization (ACO) (Dorigo et al. 2006), artificial bee colony (ABC) (Karaboga and Basturk 2007), gravitational search algorithm (GSA) (Rashedi et al. 2009), cuckoo search (CS) (Yang and Deb 2009), krill herd algorithm (Gandomi and Alavi 2012), grey wolf optimization (GWO) (Mirjalili et al. 2014), elephant herding optimization (EHO) (Wang et al. 2015), earthworm optimization algorithm (EWA) (Wang et al. 2018), Harris hawks optimization (HHO) (Heidari et al. 2019), monarch butterfly optimization (MBO) (Wang et al. 2019), Slime mould algorithm (SMA) (Li et al. 2020), Runge-Kutta optimizer (RUN) (Ahmadianfar et al. 2021), hunger games search (HGS) (Yang et al. 2021), colony predation algorithm (CPA) (Tu et al. 2021), Dwarf mongoose optimization algorithm (DMO) (Agushak et al. 2022), Tasmanian Devil Optimization (TDO) (Dehghani et al. 2022), etc. Among these algorithms, PSO is an efficient algorithm which is able to handle discrete variables fairly. ...
... Particle swarm optimization, proposed by Eberhart and Kennedy (1995), is one of the efficient among recent algorithms which is based on the swarm activities (fauna behaviour) of the living creatures such as fish schooling, bird flocking. In this algorithm, a swarm of particles is considered. ...
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The concept of computational optimization can be used to solve linear and nonlinear Diophantine equations. The goal of this article is to introduce an alternative technique, which is independent of greatest common divisor (g.c.d), to solve linear and nonlinear Diophantine equations by transforming the equations into discrete variable bound constrained optimization problems. To solve these optimization problems, weighted quantum-behaved particle swarm optimization (WQPSO) is developed into discrete variable form. Then, four linear and four nonlinear Diophantine equations are considered from recent literature and solved by using the discrete WQPSO algorithm. Next, the convergence histories of each of the problems are shown. Finally, the results are compared with the same reported by numerous researchers.
... Therefore, parameter calibration plays an essential role in improving the accuracy of Huff gravity model applications. The ordinary least squares technique (OLS), geographically weighted regression (GWR), and particle swarm optimization (PSO) are the main approaches used in the literature so far (Eberhart & Kennedy, 1995;Huff & McCallum, 2008;Liang, Gao, Cai, Foutz, & Wu, 2020;Nakanishi & Cooper, 1974;Suárez-Vega, Gutiérrez-Acuña, & Rodríguez-Díaz, 2015;Suhara et al., 2021). OLS is sensitive to outliers and GWR disregards consumer preferences by focusing on location. ...
... PSO is a continuous nonlinear optimization technique modeled after bird flocks. These particles which represent humans only use velocity and position to simulate behavior (Eberhart & Kennedy, 1995). Each particle moves to seek the optimal local location until the model converges or a maximum threshold, such as movement time, is reached (Zhan, Zhang, Li, & Shi, 2010). ...
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Many studies propose methods for finding the best location for new stores and facilities, but few studies address the store closing problem. As a result of the recent COVID-19 pandemic, many companies have been facing financial issues. In this situation, one of the most common solutions to prevent loss is to downsize by closing one or more chain stores. Such decisions are usually made based on single-store performance; therefore, the under-performing stores are subject to closures. This study first proposes a multiplicative variation of the well-known Huff gravity model and introduces a new attractiveness factor to the model. Then a forward-backward approach is used to train the model and predict customer response and revenue loss after the hypothetical closure of a particular store from a chain. In this research the department stores in New York City are studied using large-scale spatial, mobility, and spending datasets. The case study results suggest that the stores recommended being closed under the proposed model may not always match the single store performance, and emphasizes the fact that the performance of a chain is a result of interaction among the stores rather than a simple sum of their performance considered as isolated and independent units. The proposed approach provides managers and decision-makers with new insights into store closing decisions and will likely reduce revenue loss due to store closures.
... The misfit functional is slightly lower for configuration 2 and furthermore, the determined parameter configuration is slightly closer to the expected true coordinates of (−10, 20) mm. In a next step, UHSA is compared to PSO (Eberhart & Kennedy, 1995). PSO belongs to the class of evolutionary algorithms and is inspired by biological swarm intelligence. ...
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The lack of effective exploration methods during mechanized tunneling can lead to damages of the tunnel boring machine and to delays in the excavation process. Detailed knowledge about the soil properties can be gained with the aid of seismic wave propagation, where a methodology called full waveform inversion may bring higher resolutions and ranges than state-of-the-art methods. Instead of using classical adjoint full waveform inversion, Bayesian full waveform inversion is applied, where the optimization is based on Bayesian inference. In order to reduce the complexity of the objective functional, the dimensionality of the inverse problem is decreased by implementing adapted parametrizations of the subsoil models. Two methods are elaborated and validated with synthetic data as well as experimental data which is acquired in an ultrasonic laser laboratory. It is shown that the methods can deliver precise model reconstructions with a certain robustness against noise, measurement errors and modeling errors.
... In the proposed method, which is named here an "optimized multi-mode pushover analysis" (OMPA), the structural responses are obtained by integrating the results of the first to the third-mode (or the second-mode) pushover analyses using suggested modal combination coefficients. The coefficients are determined based on the IDA results using two optimization algorithms, namely particle swarm optimization (PSO) (Eberhart and Kennedy 2002) and colliding bodies optimization (CBO) (Kaveh and Mahdavi 2015), and a fitting procedure. ...
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Moment-resisting frames (MRFs) are among the most conventional steel structures for mid-rise buildings in many earthquake-prone cities. Here, a simplified performance-based methodology is proposed for the seismic collapse capacity assessment of these buildings. This method employs a novel multi-mode pushover analysis to determine the engineering demand parameters (EDPs) of the regular steel MRFs up to the collapse prevention (CP) performance level. The modal combination coefficients used in the proposed pushover analysis, are obtained from two metaheuristic optimization algorithms and a fitting procedure. The design variables for the optimization process are the inter-story drift ratio profiles resulting from the multi-mode pushover analyses, and the objective values are the outcomes of the incremental dynamic analysis (IDA). Here, the collapse capacity of the structures is assessed in three to five steps, using a modified IDA procedure. A series of regular mid-rise steel MRFs are selected and analyzed to calculate the modal combination coefficients and to validate the proposed approach. The new methodology is verified against the current existing approaches. This comparison shows that the suggested method more accurately evaluates the EDPs and the collapse capacity of the regular MRFs in a robust and easy to implement way.
... Particle swarm optimization (PSO) was first introduced by Kennedy and Eberhart in 1995. 17 The working basis of this algorithm is based on the characteristic features of animals such as a flock of birds and school of fish. 18 All members of the swarm fly to search of a potential solution in a multidimensional environment. ...
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The effect of metamaterials, whose positions are determined with particle swarm optimization (PSO) algorithm on the performance of the defected ground structure antenna is investigated. For this purpose, four different optimization studies are carried out. In Case I, the positions of the flower shape metamaterial (FSMM) structures on the antenna are determined classically at equal intervals and added to the antenna to obtain highest gain, and it is revealed that the gain is increased by 26%. In Case II, the positions and rotation angles of the FSMMs on the antenna are determined and the gain increase by 36%. In Case III, FSMMs are placed on a layer at equal intervals with the classical method. In this case, 56% increase of antenna gain is achieved. In Case IV, the positions and rotation angles of the FSMMs is formed on the lens independently of each other with PSO. In this last case, with dimensions of 24 × 36 mm², a 77% increase in antenna gain is achieved compared to the reference antenna at the resonant frequency of 12.65 GHz in dBi. As a result, it is observed that the use of metamaterials with the help of the PSO algorithm has a higher effect on antenna performance than the use of MMs with the classical method.
... The first category, "Evolutionary Algorithms," represents the algorithms that are developed based on biological reproduction and evolution, such as the Genetic Algorithm (GA) (Holland 1984), Differential Evolution (DE) (Storn and Price 1997), and Biogeography-Based Optimizer (BBO) (Simon 2008). The second category includes the algorithms that are developed based on "Swarm Intelligence," such as the Particle Swarm Optimization (PSO) (Eberhart and Kennedy 1995), Ant Colony Optimization (ACO) (Dorigo et al. 1996), and Firefly Algorithm (FA) (Yang 2012). In the third category, the "Physics-Inspired Algorithms" are the Harmony Search (HS) (Geem et al. 2001), Gravitational Search Algorithm (GSA) (Rashedi et al. 2009), Big-Bang Big-Crunch (BBBC) (Erol and Eksin 2006), Charged System Search (CSS) (Kaveh and Talatahari 2010a, b, c, d), Wind Driven Optimization (WDO) (Bayraktar et al. 2010), Multi-verse Algorithm (MVO) , Rain Fall Optimization (RFO) algorithm (Aghay Kaboli et al. 2017), Chaos Game Optimization (CGO) algorithm Azizi 2020b, 2021a), Crystal Structure Algorithm (Talatahari et al. 2021a, b, c, d, e), Material Generation Algorithm , and Atomic Orbital Search (Azizi 2021). ...
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This study proposes the Fire Hawk Optimizer (FHO) as a novel metaheuristic algorithm based on the foraging behavior of whistling kites, black kites and brown falcons. These birds are termed Fire Hawks considering the specific actions they perform to catch prey in nature, specifically by means of setting fire. Utilizing the proposed algorithm, a numerical investigation was conducted on 233 mathematical test functions with dimensions of 2–100, and 150,000 function evaluations were performed for optimization purposes. For comparison, a total of ten different classical and new metaheuristic algorithms were utilized as alternative approaches. The statistical measurements include the best, mean, median, and standard deviation of 100 independent optimization runs, while well-known statistical analyses, such as Kolmogorov–Smirnov, Wilcoxon, Mann–Whitney, Kruskal–Wallis, and Post-Hoc analysis, were also conducted. The obtained results prove that the FHO algorithm exhibits better performance than the compared algorithms from literature. In addition, two of the latest Competitions on Evolutionary Computation (CEC), such as CEC 2020 on bound constraint problems and CEC 2020 on real-world optimization problems including the well-known mechanical engineering design problems, were considered for performance evaluation of the FHO algorithm, which further demonstrated the superior capability of the optimizer over other metaheuristic algorithms in literature. The capability of the FHO is also evaluated in dealing with two of the real-size structural frames with 15 and 24 stories in which the new method outperforms the previously developed metaheuristics.
... The Prim's greedy algorithm [20] is applied to find the minimum spanning tree (MST) [21] by creating the shortest transmission paths from the sensor nodes to the sink node. Ultimately, the performance of HHO is compared with several well-known metaheuristic algorithms of the literature, such as PSO [22], flower pollination algorithm (FPA) [23], grey wolf optimizer (GWO) [24], sine cosine algorithm (SCA) [25], multi-verse optimizer (MVO) [26] algorithm, and whale optimization algorithm (WOA) [27], in terms of energy consumption, localization error, and statistical evaluation criteria. In summary, the main contributions of this paper are as follows: ...
... The Origin of PSO Particle Swarm Optimization (PSO) is a stochastic SI algorithm, invented by Russel Eberhart (electrical engineer) and James Kennedy (sociopsychologist) in 1995 [152] for solving difficult continuous optimization problems. This algorithm was originally inspired by the living world. ...
Thesis
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Nowadays, in a world covered by networks, there are more smart devices than peoples, since a person owns different smart devices in different forms. These devices, which interconnect and exchange a very large flow of data, perform several functions including monitoring, data collection, and data evaluation. In this thesis, we will focus on this new trend of interconnected objects used to improve the daily life of individuals. For this, the exploitation of the Internet of Things in the field of monitoring and control is a recent research axis that helps human beings to ensure this task based on the data captured by the intelligent devices that will be subsequently analyzed and processed by different methods. It is in this context that we orient our research on the concept of linking objects to the Internet, known today as the Internet of Things. Our work is articulated around two issues, physical activity and fall prevention in the elderly and the security of international borders. In our first work, we proposed an approach based on metaheuristics for real-time security and boundary protection. This technique is inspired by the behavior of natural cockroaches and the phenomenon of seeking the most attractive and secure place to hide. In our second work, we used classification algorithms to combat the risk of falls in the elderly and enable these individuals to continue their lives in the best possible condition. We examine the applicability of three data mining algorithms for real-world IoT datasets. These include K-nn, Naive Bayes, and Decision Tree. The main contribution of this work is the analysis of the efficiency of three data mining algorithms. All the experiments carried out and the results obtained have shown the benefits derived from the use of our system.
... PSO is another highly effective evolutionary algorithm. PSO establishes each of its particles as a potential solution to the problem being tackled (Eberhart and Kennedy, 1995). The algorithm commences with a set of random solutions applied to each particle. ...
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A key parameter when drilling for gas and oil is to determine the safe mud weight window (SMWW) to ensure wellbore stability as part of a quantitative risk assessment (QRA). This study determines SMWW by predicting acceptable upper and lower limits of the bottom hole pressure window during over-balance drilling method. A novel machine learning method is developed to predict SMWW from ten well-log input variables subject to feature selection. 3389 data records from three South Pars gas field (Iran) wells include data from: uncorrected spectral gamma ray; potassium; thorium; uranium; photoelectric absorption factor; neutron porosity; bulk formation density; corrected gamma ray adjusted for uranium content; shear-wave velocity and compressional-wave velocity. Combinations of these well logs are tuned to provide predictions of the SMWW, measured in terms of subsurface pore and fracture pressures, using machine learning (ML) algorithms hybridized with optimizers. The ML algorithms assessed are multiple layer extreme learning machine (MELM) and least squares support vector machine (LSSVM), hybridized with genetic (GA) and particle swarm (PSO) optimizers. This new algorithm (MELM) incorporates special features that improve its prediction performance, speeds up its training, inhibits overfitting and involves less optimization in the model's construction. By combining MELM with PSO, its optimum control parameters are rapidly determined. The results reveal that the MELM-PSO combination provides the highest SMWW prediction accuracy of four models evaluated. For the testing subset MELM-PSO achieves high prediction performance of pore pressure (RMSE = 12.76 psi; R² = 0.9948) and fracture pressure (RMSE = 15.71 psi; R² = 0.9967). Furthermore, the model demonstrates that once trained with data from a few wells, it can be successfully applied to predict unseen data in other South Pars gas field wells. The findings of this study can provide a better understanding of how ML methods can be applied to accurately predict SMWW.
... Présentation générale L'optimisation par essaim particulaire Eberhart and Kennedy (1995) ...
Thesis
Le projet informatique (SiSU) de l’Unité Mixe de Recherche CNRS Science pour l’Environnement conçoit des méthodes d’aide à la décision pour aider à une meilleure gestion des systèmes complexes environnementaux. Ces travaux de thèse s’inscrivent dans ce contexte. Ils ont pour objectif d'étudier les apports de plusieurs types de méthodes informatiques afin d'améliorer nos connaissances sur les systèmes complexes et ainsi de fournir une aide à leur gestion en situation de fortes incertitudes. En effet, les systèmes complexes environnementaux ne peuvent pas toujours être connus et modélisés avec précision. C’est par exemple le cas en biologie halieutique où des méthodes de gestion doivent être proposées malgré un manque de connaissances sur le système observé, dans notre cas d’étude : la pêche côtière Corse. Nos premiers travaux ont porté sur la calibration de modèles, c’est-à-dire le recherche de valeurs de paramètres permettant à nos modèles de représenter au mieux la dynamique du système. Ils ont montré les limites des approches habituelles et la nécessité d’utiliser des approches probabilistes basées sur de grandes quantités de simulations. Elles apportent une aide précieuse quant à l’acquisition de connaissances, notamment en délimitant des ensembles de solutions. Ceux-ci peuvent alors être utilisés dans des méthodes d’optimisation robuste, voire d’optimisation robuste ajustable. Ces approches permettent non seulement de prendre en compte les incertitudes, mais également de quantifier la réduction d’incertitude que de nouvelles années de données pourront apporter, afin de proposer des stratégies de plus en plus précises à long terme. L’optimisation est donc utilisable efficacement à l’échelle des décideurs. Cependant, la petite pêche côtière Corse, est un système sur lequel agissent un grand nombre d’acteurs avec des comportements différents et difficilement prévisibles et contrôlables. L’optimisation ne semble pas adaptée à l’étude de cette échelle de par la quantité de paramètres et le nombre infini de transitions stochastiques engendrées. Pour cela, des méthodes basées sur l’apprentissage profond par renforcement ont été proposées. Ces approches nous ont permis dans un premier temps de proposer un modèle gérant à la fois décideurs et pêcheurs, les uns cherchant à réduire l’impact écologique, les autres à maximiser leurs gains. À partir de cela, nous avons pu montrer que de faibles connaissances suffisent pour la maximisation des gains des pêcheurs. De plus, cette approche, couplée à de l’optimisation, a permis d’obtenir des décisions d’instauration de quotas efficaces. Enfin, ce système nous a permis d’étudier l’impact de certains comportements individuels de maximisation des gains au détriment du respect des recommandations des décideurs. Il est alors apparu que des politiques de gestion efficaces et adaptées peuvent permettre de pallier l’impact écologique d’une quantité non négligeable de ces comportements. Ainsi, nous avons pu contribuer de manière théorique à élargir les domaines d’application de la théorie de la modélisation et de la simulation, proposer un ensemble d’outils d’optimisation et d’apprentissage automatique à la gestion de systèmes dynamiques partiellement observables, mais également applicative pour la problématique de la gestion de la pêche en Corse.
... They are robust, global and can be applied generally compared to traditional optimization methods [18]. Another evolutionary computation method similar to the GA is the particle swarm optimization (PSO) [19]. It is used in turning to minimize the residual stress and forces [20]. ...
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In metastable austenitic steels like AISI 304, martensitic surface layers can be created by cryogenic external longitudinal turning which results in a hardening of the surface. It is possible to identify the correlation between process parameters and the formation of deformation-induced martensite with machine learning methods. Based on this, evolutionary algorithms are used to determine the appropriate process parameters in order to achieve different defined martensite contents. In order to be able to even control the martensite content within the turning process, an eddy current sensor is integrated into the machine tool. In-situ measurements can be conducted and are presented here.
... Particle Swarm Optimizer (PSO) was proposed in 1995 by Kennedy and Eberhart (Eberhart & Kennedy, 1995) to copy the optimization behavior seen in a group of organisms in nature like fishes and birds (called as particles) and use it to solve real-world optimization problems. In PSO, a group of particles coordinates to reach an optimal solution to a problem. ...
Article
A novel virus, called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly become a pandemic called Coronavirus disease 2019 (COVID-19). According to the World Health Organization, COVID-19 was first detected in Wuhan city in December 2019 and has affected 216 countries with 9473214 confirmed cases and 484249 deaths globally as on June 26th, 2020. Also, this outbreak continues to grow in many countries like the United States of America (U.S.), Brazil, India, and Russia. To ensure rapid surveillance and better decision-making by government authorities in different countries, it is vital to identify alive and emerging hotspots within a country promptly. State-of-the-art methods based on space-time scan statistics (like SaTScan) are not geographically robust. Also, due to the enumeration of many Spatio-temporal cylinders, the computation cost of Spatio-temporal SaTScan (ST-SaTScan) is very high. In the applications like COVID-19 where we need to detect the emerging hotspots daily as soon as the new count of cases gets updated, ST-SaTScan seems inefficient. Therefore, this paper proposes a Particle Swarm Optimizer-based scheme to timely detect geographically robust, alive, and emerging COVID-19 hotspots in a country. Timely detection can help government officials design better control strategies like increasing testing in hotspots, imposing stricter containment rules, or setting up temporary hospital beds. Performance of ST-SaTScan and proposed scheme have been analyzed for four worst-hit U.S. states for the incubation period of 14 days between June 11th, 2020, and June 24th, 2020. Results indicate that the proposed scheme detects hotspots of a higher likelihood ratio (a measure to indicate the significance of hotspot) than ST-SaTScan in significantly less time. We also applied the proposed scheme to detect the emerging COVID-19 hotspots in all states of the U.S. During the study period, the proposed scheme has detected 104 emerging COVID-19 hotspots.
... The EOSMA for single-objective problems. In EOSMA, the following improvement strategies are mainly adopted: (1) The individual and global historical optimal of PSO are introduced 81 . The individual historical optimal is preserved by greedy selection and memory mechanism. ...
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In order to solve the inverse kinematics (IK) of complex manipulators efficiently, a hybrid equilibrium optimizer slime mould algorithm (EOSMA) is proposed. Firstly, the concentration update operator of the equilibrium optimizer is used to guide the anisotropic search of the slime mould algorithm to improve the search efficiency. Then, the greedy strategy is used to update the individual and global historical optimal to accelerate the algorithm’s convergence. Finally, the random difference mutation operator is added to EOSMA to increase the probability of escaping from the local optimum. On this basis, a multi-objective EOSMA (MOEOSMA) is proposed. Then, EOSMA and MOEOSMA are applied to the IK of the 7 degrees of freedom manipulator in two scenarios and compared with 15 single-objective and 9 multi-objective algorithms. The results show that EOSMA has higher accuracy and shorter computation time than previous studies. In two scenarios, the average convergence accuracy of EOSMA is 10e−17 and 10e−18, and the average solution time is 0.05 s and 0.36 s, respectively.
... The MOPSO algorithm is regarded as an efficient algorithm, successfully utilized in various MOOPs [70,80,81]. PSO algorithm was first presented by Eberhart and Kennedy [82]. Coello et al. [83] developed the MOPSO to deal with the MOOPs by integrating the Pareto dominance and utilizing the external archive to store nondominated solutions. ...
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Dynamic customer demands impose new challenges for vehicle routing optimization with time windows, in which customer demands appear dynamically within the working periods of depots. The delivery routes should be adjusted for the new customer demands as soon as possible when new customer demands emerge. This study investigates a collaborative multidepot vehicle routing problem with dynamic customer demands and time windows (CMVRPDCDTW) by considering resource sharing and dynamic customer demands. Resource sharing of multidepot across multiple service periods can maximize logistics resource utilization and improve the operating efficiency of delivery logistics networks. A bi-objective optimization model is constructed to optimize the vehicle routes while minimizing the total operating cost and number of vehicles. A hybrid algorithm composed of the improved k-medoids clustering algorithm and improved multiobjective particle swarm optimization based on the dynamic insertion strategy (IMOPSO-DIS) algorithm is designed to find near-optimal solutions for the proposed problem. The improved k-medoids clustering algorithm assigns customers to depots in terms of specific distances to obtain the clustering units, whereas the IMOPSO-DIS algorithm optimizes vehicle routes for each clustering unit by updating the external archive. The elite learning strategy and dynamic insertion strategy are applied to maintain the diversity of the swarm and enhance the search ability in the dynamic environment. The experiment results with 26 instances show that the performance of IMOPSO-DIS is superior to the performance of multiobjective particle swarm optimization, nondominated sorting genetic algorithm-II, and multiobjective evolutionary algorithm. A case study in Chongqing City, China is implemented, and the related results are analyzed. This study provides efficient optimization strategies to solve CMVRPDCDTW. The results reveal a 32.5% reduction in total operating costs and savings of 29 delivery vehicles after optimization. It can also improve the intelligence level of the distribution logistics network, promote the sustainable development of urban logistics and transportation systems, and has meaningful implications for enterprises and government to provide theoretical and decision supports in economic and social development.
... Global-local PSO-NM algorithm: The global-local PSO-NM algorithm attempts to combine the potential capacity of the PSO method to determine the global minimum in nonconvex optimization problems with the robustness and high convergence rate of the NM scheme. The PSO algorithm is based on swarm intelligence and mimics the social behavior of populations [34,35]. The method defines a population of candidate solutions to which inertia, cognitive and social operators are applied, causing the swarm to move in the decision space and flocking toward the global minimum. ...
Article
This work addresses a modelling framework for identification of material parameters based on multi-objective optimisation aiming at multiple mechanical tests or experimental data sets. The multi-objective problem is described within the Pareto theory, from which a strategy based on the global method solved in a normalised feasible objective space is proposed. The method maps the objective space onto a new normalised space where the optimal solution is found by minimising a global error function. The strategy is illustrated by identification of hardening and damage parameters based on tensile and compression tests featuring the effects of tensile-dominant and compressive-dominant stress states.
... The class of swarm algorithms, Particle Swarm Optimization (PSO) [56], Ant Colony Optimization (ACO) [57], Bat Algorithm (BA) [58] and Bird Swarm Algorithm (BSA) [59] are characterized by the displacement of individuals observing the search for food made by animals. In this case, leadership characteristics and individuals closest to the prey are applied to change their position and speed in the optimization process. ...
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UAVs have been widely used in many photography applications. Among photography tasks, covering an area to produce photogrammetry data is a prominent solution for commercial applications. The resources available to automate these processes, despite the many advances, still are somewhat scarce for practical deployment. In this research, the authors intend to bridge this gap by evaluating several simulated and real 3D models. The methods evaluated are also compared with current commercially available planners and the literature state of the art. The research uses seven heuristics methods to generate camera placement, another seven methods to generate offline planning, and seven methods to perform path planning. The tests include synthetic data for statistical significance, real 3D models, and simulations that allow a complete performance overview. Quantitative results will enable the user to visualize each method’s performance, while qualitative results will help to understand the results visually. Results are compiled in a path planning library for further research and development. They show that some methods can be 15% more cost-effective while being able to be still computed in a reasonable amount of time.
... They are extensively used to solve engineering and scientific problems. Some swarm-based algorithms are Particle Swarm Optimization (PSO) [17], Bacterial Foraging Optimization (BFA) [26], Salp Swarm Algorithm (SSA) [28], etc. Among these bio-inspired optimization algorithms, BFA is based on the Escherichia Coli (E. ...
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Bacterial foraging algorithm (BFA) is a novel nature-inspired algorithm that mimics the social foraging behavior of E. coli. Bacteria. However, it gets stuck in the local optima trap and yields poor convergence in complex landscapes. To improve the exploration-exploitation balance and achieve the global optima quickly, this paper proposes a novel hybrid called the Bacterial foraging algorithm-firefly algorithm (BFA-FA). In this work, two strategies namely adaptive strategy and leadership strategy are applied on conventional BFA. The performance is examined on standard, non-linear and CEC_2017 benchmark functions over several evaluation parameters. The results on benchmark functions show that BFA-FA provides accurate solutions, avoids local optima, works well on multimodal and multidimensional landscapes, and converges faster. It also shows the statistically significant difference among other algorithms. The proposed algorithm is applied on two classical engineering problems to validate its robustness and applicability.
... In this paper, the best ML approach between ANN, KNN, and SVM is optimized through nature-based optimization algorithms to discriminate the damaged and healthy structures according to the entropy-based damage-sensitive features [31]. The optimization algorithms used in this paper are the particle swarm optimization (PSO) [32,33], the Shuffled frog leaping algorithm (SFLA) [34], and the Salp swarm algorithm (SSA) [35]. ...
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This paper proposes an output-only damage classification framework using correlation functions and information entropy. Information entropy quantifies the amount of complexity and irregularity present in a signal; thus, entropy is assumed to be related to the structural behavior changes caused by damage. Damage-sensitive feature vectors are entropies of the structural response which are used to train machine learning (ML) models (such as KNN, ANN, and SVM) for damage classification. This paper uses an output-only correlation-based alternative of the acceleration signal to calculate entropies and derive feature vectors. The studies are done on a standard experimental dataset of a three-story frame at the Los Alamos National Lab. It was observed that entropies of the proposed signal can accurately identify linear and nonlinear damage samples. Furthermore, KNN, ANN, and SVM are used to identify the damage, and SVM proved to outperform other ML techniques in entropy-based damage identification. To enhance the SVM’s performance, three nature-based optimization algorithms were hybridized with this method (i.e., SVM-PSO, SVM-SFLA, and SVM-SSA). The performance of these methods was evaluated and the SVM-SSA model proved to be the most reliable method in detecting damage with an accuracy of 93.98% for the testing data.
... The PSO method, originally proposed by Eberhart and Kennedy (1995), was inspired by the social behavior of a bird flock. The application of PSO search algorithms is favored in various fields for solving optimization problems owing to their simplicity, easy implementation, and robustness (Vaz Jr et al., 2016). ...
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Accurate prediction of ductile fracture requires determining the material properties, including the parameters of the constitutive and ductile fracture model, which represent the true material response. Conventional calibration of material parameters often relies on a trial-and-error approach, in which the parameters are manually adjusted until the corresponding finite element model results in a response matching the experimental global response. The parameter estimates are often subjective. To address this issue, in this paper we treat the identification of material parameters as an optimization problem and introduce the particle swarm optimization (PSO) algorithm as the optimization approach. We provide material parameters of two uncoupled ductile fracture models-the Rice and Tracey void growth model (RT-VGM) and the micro-mechanical void growth model (MM-VGM), and a coupled model-the Gurson-Tvergaard-Needleman (GTN) model for ASTM A36, A572 Gr. 50, and A992 structural steels using an automated PSO method. By minimizing the difference between the experimental results and finite element simulations of the load-displacement curves for a set of tests of circumferentially notched tensile (CNT) bars, the calibration procedure automatically determines the parameters of the strain hardening law as well as the uncoupled models and the coupled GTN constitutive model. Validation studies show accurate prediction of the load-displacement response and ductile fracture initiation in V-notch specimens, and confirm the PSO algorithm as an effective and robust algorithm for seeking ductile fracture model parameters. PSO has excellent potential for identifying other fracture models (e.g., shear modified GTN) with many parameters that can give rise to more accurate predictions of ductile fracture. Limitations of the PSO algorithm and the current calibrated ductile fracture models are also discussed in this paper.
... In [45], the HBA has been tested using benchmark functions with diverse properties and real-world engineering problems. According to [45], the HBA is superior to other metaheuristics proposed in the literature, among them, PSO [49], MFO [50], WOA [51], GOA [52] and HHO [10]. In solving real-world problems [45], the HBA has shown superior performance compared to the recently proposed metaheuristics [11,46,47]. ...
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This paper proposes a modified honey badger algorithm (MHBA) for solving the optimal power flow (OPF) problem. This problem is a highly non-linear, non-convex and complex optimization problem with several decision variables and constraints. The original honey badger algorithm (HBA) has the problem of trapping in local optima due to the loss of population diversity, especially in solving complex optimization problems. Therefore, the MHBA aims at sufficient improvement in finding the optimal solution and feasibility. Opposition-based learning strategy (OBL) is integrated with the MHBA to preserve the diversity of the population and enhance the convergence toward the optimal solution. The effectiveness of the MHBA algorithm is evaluated on five objective functions of the OPF problem namely, total generation fuel cost minimization, active power and reactive power transmission losses minimization, voltage deviation and voltage stability enhancement. The performance of the proposed algorithm is tested and validated on the IEEE 30-bus test system. The proposed MHBA is compared with the HBA and other nature-inspired optimization algorithms reported in the literature. The results indicate that the proposed MHBA algorithm has the superiority to jump out of the local optimal and better convergence in solving the OPF problem. This is due to the strategy used in the algorithm which helps in maintaining the population diversity and provides a proper balance between exploration and exploitation.
... f target = f opt +Δf is target function value to reach for different Δf values. Following algorithms: ALPS (Age-Layered Population Structure) [59], BayEDa [60], BFGS (Broyden-Fletcher-Goldfarb-Shanno) [61], CMA-ESOLUSSEL ((1 + 1)-CMA-ES) [62], DE-PSO (Hybrid particle swarm with differential operator), EDA-PSO [63], FTL [7], FULLNEWUOA (NEW Unconstrained Optimization Algorithm) [64], GLOBAL [65], MCS (multilevel coordinate search) [66], NEWUOA (NEW Unconstrained Optimization Algorithm) [67], PSO [68], PSO_-Bounds [63] and Random search [62] has been compared with iFTL using the COCO framework. The data for these optimization algorithms are available at http : //coco.gforge.inria.fr/. ...
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Swarm-based models mimic the collective behavior shown in insects or animals. To date, several algorithms have been proposed by researchers to solve a wide range of complex optimization problems. This paper presents an improved version of follow the leader (iFTL) algorithm that imitates the behavioral movement of a sheep within the flock. The work presented in this paper mathematically models this foraging behavior to realize the process of optimization. The COmparing Continuous Optimisers (COCO) experimental framework is used for performance evaluation with twenty-four noiseless and thirty noisy benchmark functions. After that, it has been compared with fourteen well-presented optimization algorithms in different dimensions. The results generated show that iFTL outperformed all compared optimization algorithms and outranked in all dimensions. The iFTL algorithm is also tested on twenty-four standard benchmark function and compared with eight well-known optimization algorithms to benchmark its performance. Further, the efficacy of the proposed algorithm is tested on 10, 37, 52, 72, 120, 200, 224, and 942 bar truss design problems. Finally, the results generated by truss design problems are compared with other meta-heuristics algorithms to validate the performance of the proposed algorithm. The obtained results reveal that iFTL is efficient and stable than other state-of-the-art algorithms.
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Nowadays, the explosive growth in text data emphasizes the need for developing new and computationally efficient methods and credible theoretical support tailored for analyzing such large‐scale data. Given the vast amount of this kind of unstructured data, the majority of it is not classified, hence unsupervised learning techniques show to be useful in this field. Document clustering has proven to be an efficient tool in organizing textual documents and it has been widely applied in different areas from information retrieval to topic modeling. Before introducing the proposals of document clustering algorithms, the principal steps of the whole process, including the mathematical representation of documents and the preprocessing phase, are discussed. Then, the main clustering algorithms used for text data are critically analyzed, considering prototype‐based, graph‐based, hierarchical, and model‐based approaches. This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Clustering and Classification Statistical Learning and Exploratory Methods of the Data Sciences > Text Mining Data: Types and Structure > Text Data Document clustering: Prototype‐based, Graph‐based, Hierarchical and Model‐based methods
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As the energy market has grown in importance in recent decades, researchers have paid increasing attention to swing option contracts. Early studies evaluated the swing contract as if it were a financial derivative contract, by ignoring its storage constraints. Aided by recent advances in artificial intelligence (AI) and machine learning (ML) technologies, recent studies were able to incorporate storage limitations. We make two discoveries in this paper. First, we contribute to the literature by proposing an AI methodology—particle swarm optimization (PSO)—for the evaluation of the swing contract. Compared to the other ML methodologies in the literature, PSO has an advantage by expanding to include more features. Secondly, we study the relative impact of the price process (exogenously given) that underlies the swing contract and the storage constraints that affect a quantity decision process (endogenously decided), and discover that the latter has a much greater impact than the former, indicating the limitation of the earlier literature that focused only on price dynamics.
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Increasing concerns over environmental pollution across the globe have encouraged us to replace some conventional products with green products. The production cost for green products being higher, the governments in various countries have initiated subsidy policies for green product manufacturers. The carbon regulatory authorities in different nations have started carbon taxation policies to lower the emission. Investment in emission reduction technologies can control the emission of carbon from a manufacturing firm. This paper explores the impacts of joint investment in greening innovation and emission reduction technology in a green production inventory model and provides some better insights to the real-life practitioner. Assuming a selling price and greenness level dependent demand, the optimal inventory decisions are examined under the cap and trade carbon regulatory policy. The model also considers the possibility of defective production and their repairing process. The aim is to find the optimal selling price, the optimal degree of greenness, optimal emission reduction technology investment, and optimal production run time that maximizes the optimal profit. Numerical illustration is presented to validate the model. Sensitivity analysis of the optimal solutions concerning the key inventory parameters is conducted for identifying several managerial implications. It is found that higher subsidy intensity increases the degree of greenness of the product. It is also seen that the simultaneous investment in greening innovation and emission reduction technology is beneficial for the green product manufacturer and the environment.
Chapter
Photo voltaic (PV) systems need to be operated in maximum power point (MPP) for extracting optimum power from it. However, during partial shading conditions (PSC) the P–V curve exhibits multiple peaks, and it becomes essential to track the real maximum peak (global maxima). This paper proposes an improved particle swarm optimization (PSO)-based algorithm for tracking MPP during PSC in the PV system. The algorithm incorporates dynamic control where the parameters are dynamically varied with the iteration number. Additionally, a modified initialization of particles in search space and a jump-out algorithm is proposed for faster convergence to global maxima. The proposed algorithm is compared with two other recently developed PSO-based algorithms for different test cases. The simulation (simulated on MATLAB/Simulink) results show that the proposed algorithm converges to accurate global maxima for all partially shaded conditions with an accuracy of 99% with faster tracking time compared to other two algorithms.
Chapter
Heart disease is one of the foremost health problems nowadays, and deadliest human disease around the world. It is the main reason for the enormous range of deaths in the world over the previous few decades. Therefore, there is a need to diagnose it in an exceeding specific time to avoid abandoned dangers. In this paper, we propose a hybrid approach to heart disease prediction by using a given range of feature vectors. Furthermore, a comparison of several classifiers for the prediction of heart disease cases with a minimum number of feature vectors are carried out. We proposed two different optimization algorithms like genetic algorithm (GA), and particle swarm optimization (PSO) for feature selection, and convolution neural network (CNN) for classification. The hybrid of GA and CNN is known as genetic neural network (GCNN), and hybrid of PSO and CNN now as particle neural network (PCNN). The experimental results show that accuracy values obtained by PCNN is approximately 82% and GCNN is 75.51%.
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This paper proposes an improved variant of the arithmetic optimization algorithm (AOA), called LMRAOA, which is used to solve numerical and engineering problems. Various strategies are proposed to improve AOA. First, Multi-Leader Wandering Around Search Strategy (MLWAS) is proposed to improve the exploration ability of the algorithm on global scale. Then, Random High-Speed Jumping Strategy (RHSJ) is proposed, and the search agent performs high-speed search in the current neighborhood to improve the exploitation ability. Finally, in order to avoid local optima, adaptive lens opposition-based learning strategy is proposed, and linear changes are proposed in its parameters to further satisfy the dynamic changes. 27 classic benchmark functions, 6 engineering optimization problems, and CEC2014, CEC2019 and CEC2020 competition functions are tested by LMRAOA algorithm and comparison algorithm. The experimental results show that, in most cases, the LMRAOA outperforms other algorithms in solving engineering and numerical problems, and can provide effective solutions.
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Oceanic surface buoys are known to have energetic demands for long-term stays at sea. Currently, batteries and solar panels have been use to cope with this need. Nonetheless, they are prone to damage and need replacement. Moreover, solar panels only function with sunlight, thus reducing their reach to some regions. Triboelectric nano-generators (TENG) are energy harvesting devices, capable of converting mechanical energy into electricity, by triboelectric effect and electrostatic induction. Their high efficiency, low manufacturing price and high power density make it ideal for wave energy conversion. In this paper, based on laboratory results obtained for a TENG model on a navigational buoy, a voltage production estimation model was generated using Particle Swarm Optimization (PSO), which can be fed with Boundary Element Method (BEM) simulations as input. The conclusion stated that PSO is a reliable methodology allowing for a quick estimation of voltage output for a given geographical region. It also becomes clear that coupling with existing BEM software is capable.
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This paper presents a novel descent algorithm based on the step-by-step iterative principle, applied to the optimum design of steel frames. The search consists on finding the direction which decreases the structural weight most quickly. As the design problem includes discrete variables, the optimum is found by evaluating the structural weight gradient step by step. The step size is controlled in such a way that convergence towards infeasible or suboptimal solutions is avoided. By properly choosing the initial solution, it is possible to increase the efficiency and the convergence speed of the algorithm. Many strategies, for the choice of initial design point, by making use of engineering intuitions or using optimized design obtained by other algorithms are discussed. Furthermore, it is confirmed in this study that the proposed algorithm can be used to improve optimum designs found by metaheuristic algorithms. The optimization results, relative to several weight minimizations problems of benchmark planar steel frames designed according to Load and Resistance Factor Design, American Institute of Steel Construction (LRFD-AISC) specifications, are compared to those obtained by different optimization methods. The comparison proves the efficiency and robustness as well as the prompt of convergence of the proposed descent algorithm developed in this paper.
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Optimal foraging algorithm (OFA) is a newly stochastic optimization technique and is famous for its computational accuracy. However, the high computational accuracy leads to slow convergence speed. Experimental results demonstrate that OFA is good at unimodal functions but poor at multimodal functions. To improve these drawbacks, in this paper a novel modified OFA with direction prediction and gaussian oscillation, named OFA/P&G is introduced. In OFA/P&G, a transition matrix is constructed when a new global optimum is found to generate the candidate individuals. If the current global optimum does not change, the Gaussian oscillation is employed in a low probability and OFA update method is used in a high probability to generate the candidate individuals. The superior performance of OFA/P&G is verified on the 12 CEC2017 benchmark functions, 13 constrained benchmark functions and 5 engineering problems. Experimental results demonstrate that OFA/P&G outperforms other comparative algorithms. Finally, a real-world problem, drilling path optimization, is solved by OFA/P&G.
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A recently developed metaheuristic optimization algorithm, Salp Swarm Algorithm (SSA), has manifested its capability in solving various optimization problems and many real-life applications. SSA is based on salps’ swarming behaviour when finding their way and searching for food in the oceans. Nonetheless, like most metaheuristic algorithms, SSA experiences low convergence and stagnation in local optima and rate. There is a need to enhance SSA to speed its convergence and effectiveness to solve complex problems. In the present study, we will introduce chaos into SSA (CSSA) to increase its global search mobility for robust global optimization. Detailed studies are carried out on real-world nonlinear benchmark systems and CEC 2013 benchmark functions with chaotic map (Tent). Here, the algorithm utilizes a Tent map to tune the salp leaders’ attractive movement around food sources. The experimental results, considering both convergence and accuracy simultaneously, demonstrate the effectiveness of CSSA for 12 nonlinear systems and 28 unconstrained optimization problems CEC 2013. Two nonparametric statistical tests, the Friedman test and Wilcoxon Signed- Rank Test, are conducted to show the superiority of CSCA over other states of the art algorithms and our results’ significance.
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The Ubiquity of Chaos
  • Krasner
  • Ed
  • H Si J
  • Holland
Krasner, Ed., The Ubiquity of Chaos, AAAS Publications, Washington, DC, 1990. [SI J. H. Holland, Adaptation in Natural and Artificial Systems, MIT Press, Cambridge, MA., 1992.