No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Elephant Herding Optimization
Abstract
In this paper, a new kind of swarm-based metaheuristic search method, called Elephant Herding Optimization (EHO), is proposed for solving optimization tasks. The EHO method is inspired by the herding behavior of elephant group. In nature, the elephants belonging to different clans live together under the leadership of a matriarch, and the male elephants will leave their family group when they grow up. These two behaviors can be modelled into two following operators: clan updating operator and separating operator. In EHO, the elephants in each clan are updated by its current position and matriarch through clan updating operator. It is followed by the implementation of the separating operator which can enhance the population diversity at the later search phase. To demonstrate its effectiveness, EHO is benchmarked by fifteen test cases comparing with BBO, DE and GA. The results show that EHO can find the better values on most benchmark problems than those three metaheuristic algorithms.
... Wang et al. [35] have proposed the Elephant Herding Optimization (EHO) algorithm, inspired by elephant herding behavior, featuring a clan updating operator and a clan separating operator for updating the positions of elephants and matriarchs within clans. ...
... . The convergence of the COVO algorithm is compared with EHO [35], SSO [40], SSA [44], SFO [42], BOA [41], BWO [43], SMO [32], CVOA [47], SRO [53], and GBRUN [54] respectively. The error in fitness value acquired for the 13 standard benchmark functions is shown in Figure 5. ...
... The fitness function 2 F is lower with COVO, even under higher variation in the count of the fitness evaluations. The error in fitness function 13 F is lower than the existing models like EHO [35], SSO [40], SSA [44], SFO [42], BOA [41], BWO [43], SMO [32], CVOA [47], SRO [53], and GBRUN [54] respectively. The convergence performance of various optimization methods compared to the proposed COVO algorithm shows varying levels of effectiveness. ...
The metaheuristic optimization technique attained more awareness for handling complex optimization problems. Over the last few years, numerous optimization techniques have been developed that are inspired by natural phenomena. Recently, the propagation of the new COVID-19 implied a burden on the public health system to suffer several deaths. Vaccination, masks, and social distancing are the major steps taken to minimize the spread of the deadly COVID-19 virus. Considering the social distance to combat the coronavirus epidemic, a novel bio-inspired metaheuristic optimization model is proposed in this work, and it is termed as Social Distancing Induced Coronavirus Optimization Algorithm (COVO). The pace of propagation of the coronavirus can indeed be slowed by maintaining social distance. Thirteen benchmark functions are used to evaluate the COVO performance for discrete, continuous, and complex problems, and the COVO model performance is compared with other well-known optimization algorithms. The main motive of COVO optimization is to obtain a global solution to various applications by solving complex problems with faster convergence. At last, the validated results depict that the proposed COVO optimization has a reasonable and acceptable performance.
... Despite various pruning strategies designed to enhance efficiency, these algorithms are still constrained by high memory usage. To address these issues, this paper designs a data stream HUIM method based on elephant herd optimization (EHO) [9]. The main contributions are: ...
... To address various global optimization problems, Wang et al. [9], inspired by the social behavior of elephant herds, first proposed the EHO algorithm in 2015. Due to the fact that the elephants of the clan live under the leadership of the female elephant, the position of each elephant j in the clan c i is influenced by the female elephant, updated by formula (6). ...
... However, with the iterative updates of the population, the optimal individual from the current iteration may replace the optimal individual from the previous iteration. Considering the influence of excellent individuals in the current iterative population, similar to the particle swarm optimization algorithm, SHUIM-EHO (naive) algorithm uses formula (9) to update the clan. In the formula, x pbest,ci represents the position vector of the best individual in the current iterative population, andα, β and λ are all random numbers of [0,1]. ...
Mining high utility itemsets from data stream within limited time and space is a challenging task. Traditional algorithms typically require multiple scans and complex data structures for data connection, storage and update. Moreover, the evaluation of duplicate itemsets generated by overlapping batches leads to low efficiency of the algorithm in terms of time and space. To address these issues, this paper proposes a heuristic-based data stream high utility itemset mining algorithm, termed SHUIM-EHO, designed to effectively solve limited storage space. The SHUIM-EHO algorithm designs a new clan updating strategy, which effectively enhances the convergence speed and reduces itemset loss. Additionally, a hash storage strategy is proposed to avoid the evaluation of duplicate itemsets, thereby further improving the execution efficiency of the algorithm. Experiments on real and synthetic datasets demonstrate the effectiveness of the algorithm, significantly reducing memory consumption and maintaining strong scalability.
... Swarm based meta heuristic algorithms are widely applied to multiple problems and desirable results are achieved due to their varied strengths, i.e., fast convergence and achieving global optimum results. EHRE is a type of heuristic algorithm that is based on swarm based meta heuristic search methods [151,152,153,154]. It is designed for solving complex optimization problems. ...
... It is designed for solving complex optimization problems. EHRE is influenced by the elephant's herding behavior [151,152,153,154]. It has many multiple evolutionary characteristics that are mostly not present in the conventional heuristic algorithms that include rapid convergence rate, converging capability towards global optimum results by exploring hidden strengths of population space and maintaining population diversity through multiple clans. ...
... Matriarch is the fittest elephant in each clan and its value is updated through equation 5.5 [151]. ...
Increase in growth of Internet of Things (IoT) enabled applications; lead to rapid increase in IoT empowered devices. This rapid growth of devices will contribute to complexity of IoT network. Increase in network complexity will intensify the risk against topology robustness. IoT act as a core enabler; for converting conventional city to smart; having multiple type of devices. These devices produce large amount of variant data types; including node sensing information, node geographic location data and connected neighbors, etc. Improving topology robustness of emerging IoT networks for smart cities by using big data against cyber, intentional, targeted and malicious attacks has become a prime issue. To cater the topology robustness issue by using big data, seven algorithms, i.e., Enhanced Angle Sum Operation EASO-ROSE, Enhanced ROSE, Adaptive Genetic Algorithm (AGA), Cluster Adaptive Genetic Algorithm (CAGA), Enhanced Differential Evolution (EDE), Adaptive EDE (AEDE) and Elephant Herding Robustness Evolution (EHRE) algorithm are proposed for smart cities. Proposed solutions keep the node initial degree distribution or edge density of deployed nodes unchanged, along with maintaining the scale-free property of the topology. IoT sensors are relieved from the computational overhead of algorithms by using a robust proposed system model. Enhanced ROSE, EASO-ROSE, CAGA and AGA performs 61.3% and 48.3%, 45.5% and 34.95% better as compare to simulating annealing. The EDE performs 7.13%, 31.6% and 41.8% better as compared to GA, SA and HA, respectively. The AEDE outperforms the GA, SA and HA with 11%, 35.3% and 45.4% better efficiency, respectively. EHRE achieves 95% efficiency after 60 iterations and 99% efficiency after 70 iterations. Moreover, EHRE performs 58.77% better than EDE, 65.22% better than GA, 86.35% better than SA and 94.77% better than HA. The thesis is based on the evolutionary research.
... The intriguing social interactions among animals have captured the interest of researchers, leading to the development of innovative techniques in this field in recent years [20][21][22]. Grey Wolf Optimization (GWO) [23], Whale Optimization Algorithm (WOA) [24], Bat-inspired Algorithm (BA) [25], Grasshopper Optimization Algorithm (GOA) [26], and Elephant Herding Optimization (EHO) [27] are among the various innovative approaches. Another set of MH techniques (3) comprises physics-based methods that draw inspiration from the fundamental laws of physics governing the universe. ...
... Some experts contend that having a leader with diverse duties and tasks, who guides individuals toward the optimal solution, enhances the speed of convergence. In algorithms such as GWO [23], Pathfinder algorithm [53], EHO [27], and Naked Mole-Rat Algorithm (NMRA) [54], a hierarchical structure is present with a leader steering the population. Another progressive strategy that has experienced growth in recent years is segmenting the population into smaller cohorts with distinct objectives and responsibilities, and subsequently pursuing the search process with these cohorts. ...
Metaheuristics are commonly used in various fields, including real-life problem-solving and engineering applications. The present work introduces a novel metaheuristic algorithm named the Artificial Circulatory System Algorithm (ACSA). The control of the circulatory system inspires it and mimics the behavior of hormonal and neural regulators involved in this process. The work initially evaluates the effectiveness of the suggested approach on 16 two-dimensional test functions, identified as classical benchmark functions. The method was subsequently examined by application to 12 CEC 2022 benchmark problems of different complexities. Furthermore, the paper evaluates ACSA in comparison to 64 metaheuristic methods that are derived from different approaches, including evolutionary, human, physics, and swarm-based. Subsequently, a sequence of statistical tests was undertaken to examine the superiority of the suggested algorithm in comparison to the 7 most widely used algorithms in the existing literature. The results show that the ACSA strategy can quickly reach the global optimum, avoid getting trapped in local optima, and effectively maintain a balance between exploration and exploitation. ACSA outperformed 42 algorithms statistically, according to post-hoc tests. It also outperformed 9 algorithms quantitatively. The study concludes that ACSA offers competitive solutions in comparison to popüler methods.
... To address this challenge, this research proposes a modified Elephant Herding Optimization (EHO) algorithm for the optimal sizing and placement of series capacitors in distribution networks [14]. The EHO algorithm is a population-based metaheuristic optimization technique inspired by the herding behavior of elephants in nature [15]. By adopting a modified version [16] of this algorithm to the problem of series capacitor optimization, this work aims to effectively determine the most suitable locations and sizes of series capacitors to enhance system voltage stability under different loading conditions. ...
... The MEHO algorithm [16] is an improved version of the original EHO algorithm [15] that emulates the collective behavior of elephants in their natural habitat, specifically their herding and foraging patterns. It consists of two main phases: the clan updating operator and the separating updating operator. ...
The optimal size and location of series capacitors is a critical challenge in a distribution network. In this paper, a novel approach for enhancing voltage stability in distribution networks through the optimal sizing and placement of series capacitors is proposed. The study introduces a technique to determine the optimal lines for connecting series capacitors based on line reactance and current. A Modified Elephant Herding Optimization (MEHO) algorithm was used to determine the reactance sizes of the series capacitors and the best lines to place them for optimum system performance. To evaluate the effectiveness of the proposed method, three series capacitors are placed and sized in the standard IEEE 33-bus radial distribution system for stability enhancement. A comparison is conducted between the proposed MEHO algorithm-based approach, the original Elephant Herding Optimization (EHO) algorithm, and the IGWO-TS-based methods reported in the literature. The evaluation is performed by analyzing the system voltage profile, total system losses, and system voltage deviation index under varying loading conditions of 30%, 100%, and 120% of the system nominal loading. Results demonstrate that the proposed MEHO algorithm-based approach outperforms the other two methods significantly in all the scenarios, highlighting its effectiveness in voltage stability enhancement in distribution networks., "Optimal sizing and placement of series capacitors in distribution networks using modified elephant herding optimization algorithm,"
... Many other algorithms, apart from PSO and QPSO, also incorporate the mean strategy. At the 2015 International Computing and Business Intelligence Conference, Wang et al. proposed the Elephant Herding Optimization algorithm (EHO) [15], in which the elephant migration behavior with the best fitness was entirely influenced by the mean vector of elephants. In 2017, Cheung et al. enhanced the CS algorithm [16] by integrating a quantum model into the CS algorithm and introducing a non-homogeneous search strategy based on the quantum mechanism. ...
... Equation (9) shows that by comparing the values of the two samples before and after, the slope is obtained. Under the QDF, the values of two adjacent samples are expressed by energy E a and E b (E a is the low-energy state and E b is the high-energy state), respectively, according to the energy superposition Equation (15), and the superposition of n energy states in the formula is approximately the superposition of two energy states E a and E b . That is the two-energy level approximation (TELA). ...
The method of quantum dynamics is employed to investigate the mean strategy in the swarm intelligence algorithm. The physical significance of the population mean point is explained as the location where the optimal solution with the highest likelihood can be found once a quantum system has reached a ground state. Through the use of the double well function and the CEC2013 test suite, controlled experiments are conducted to perform a comprehensive performance analysis of the mean strategy. The empirical results indicate that implementing the mean strategy not only enhances solution diversity but also yields accurate, efficient, stable, and effective outcomes for finding the optimal solution.
... The fitness function value depends on the virtual machine MIPS, and the size of the user request submitted. In the domain of artificial intelligence domain, the neural classifier efficiency depends on the elephant behavior-inspired optimization technique [25]. The Elephant Herd Optimizer takes input parameters, including the number of epochs, population size, task counts, and virtual machines submitted for execution. ...
... Clan updating and separation operations are executed through fitness function operations. The updating and separation operations are performed until the termination condition is reached [25]. Figure 2 illustrates the flow diagram of the Elephant Herd Optimization approach. ...
Resources are offered to customers on demand in the modern era of computing, communication, and technology. User demand for the resources depends on the service provider and consumer. The optimal assignment of the cloud resources depends on fitness function and resource management technique. In this manuscript, the key focus is to propose a model based on a meta-heuristic evaluation technique. The meta-heuristic evaluation technique provides optimal placement of the virtual machines to the user requests across the globe. The presented framework, elephant heard optimization with neural network (EHO-ANN) outperforms the existing static, dynamic, and nature-inspired techniques. The EHO-ANN is evaluated and analyzed against the Harmony Search Approach, Elephant Heard Optimizer, BAT, and GA cost-aware approach. The evaluation and analysis include the performance metrics, average Execution Time (ms), average Start Time (ms), average utilization, and average Finish Time (ms). The presented model EHO-ANN is validated using two configuration scenarios with 10 virtual machines and 5 virtual machines. The results are generated by fifteen times repeated experimentation which assures the accuracy of the model.
... Over the past few decades, numerous swarm intelligence algorithms have been suggested, including Sparrow Search Algorithm (SSA) [12], Elephant Herding Optimization (EHO) [13], Snake Optimizer (SO) [14], Dung Beetle Optimizer (DBO) [15], Whale Optimization Algorithm (WOA) [16], Harris Hawks Optimization (HHO) [17]. These algorithms possess a straightforward structure, are easily implemented, exhibit resilience, and are extensively employed in resolving diverse intricate optimization problems. ...
... Let the scaling factor k = h h * , then the inverse point x * can be obtained by the transformation as shown in Eq. (13). ...
In order to enhance the global search ability of the sand cat swarm optimization, avoid falling into the local optimum at a later stage, and improve the performance of the algorithm, an improved algorithm is proposed - Improved sand cat swarm optimization based on lens opposition-based learning and sparrow search algorithm (LSSCSO). A dynamic spiral search is introduced in the exploitation stage to make the algorithm search for better positions in the search space and improve the convergence accuracy of the algorithm. The lens opposition-based learning and the sparrow search algorithm are introduced in the later stages of the algorithm to make the algorithm jump out of the local optimum and improve the global search capability of the algorithm. To evaluate the effectiveness of LSSCSO in solving global optimization problems, the performance of the algorithm is tested using 23 standard benchmark functions and compared with seven competitive algorithms, which show that LSSCSO has strong optimality finding ability and performs optimally in most cases. Finally, the application of LSSCSO to four engineering optimization problems also verifies the effectiveness of the algorithm in solving engineering optimization problems.
... EHA introduced by Wang et al., 48 is a powerful technique inspired by herd behavior in elephants. It excels at identifying the most informative features from a large dataset, making it well-suited for feature selection in diabetes diagnosis using gene expression data. ...
Diabetes Mellitus (DM) is a global health challenge, and accurate early detection is critical for effective management. The study explores the potential of machine learning for improved diabetes prediction using microarray gene expression data and PIMA data set. Researchers utilizing a hybrid feature extraction method such as Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO) followed by metaheuristic feature selection algorithms as Harmonic Search (HS), Dragonfly Algorithm (DFA), Elephant Herding Algorithm (EHA). Evaluated the performance of a system by using the following classifiers as Non-Linear Regression—NLR, Linear Regression—LR, Gaussian Mixture Model—GMM, Expectation Maximization—EM, Bayesian Linear Discriminant Analysis—BLDA, Softmax Discriminant Classifier—SDC, and Support Vector Machine with Radial Basis Function kernel—SVM-RBF classifier on two publicly available datasets namely the Nordic Islet Transplant Program (NITP) and the PIMA Indian Diabetes Dataset (PIDD). The findings demonstrate significant improvement in classification accuracy compared to using all genes. On the Nordic islet transplant dataset, the combined ABC-PSO feature extraction with EHO feature selection achieved the highest accuracy of 97.14%, surpassing the 94.28% accuracy obtained with ABC alone and EHO selection. Similarly, on the PIMA Indian diabetes dataset, the ABC-PSO and EHO combination achieved the best accuracy of 98.13%, exceeding the 95.45% accuracy with ABC and DFA selection. These results highlight the effectiveness of our proposed approach in identifying the most informative features for accurate diabetes prediction. It is observed that the parametric values attained for the datasets are almost similar. Therefore, this research indicates the robustness of the FE and FS along with classifier techniques with two different datasets.
... Hybrid SC techniques along with nature-inspired methods are playing a vital role nowadays. Some of them are Gray colored wolf based technique for optimization (Mirjalili, Mirjalili, and Lewis 2014), Crow behavior-inspired searching technique derived algorithm (Zolghadr-Asli, Bozorg-Haddad, and Chu 2018), Hawks based derived algorithm for optimization (Debruyne and Kaur 2016), an Artificial method of feeding based algorithm for birds (Lamy 2019), Ants, as well as lion, inspired algorithms for optimization (Mirjalili 2015), Snake inspired technique for optimization (Naghdiani and Jahanshahi 2017), Spot by hyena method for optimization (Dhiman and Kumar 2019), Elephant deriving the steering for optimization (Wang, Deb, and Coelho 2015), Penguins emperor derived from the colony (Harifi et al. 2019), Whale behavior-based algorithm for optimization (Mirjalili and Lewis 2016). Most of these techniques are used for the identification of the various psychological behaviors of human beings. ...
Soft computing is a collective methodology that touches all engineering and technology fields owing to its easiness in solving various problems while comparing the conventional methods. Many analytical methods are taken over by this soft computing technique and resolve it accurately and the soft computing has given a paradigm shift. The flexibility in soft computing results in swift knowledge acquisition processing and the information supply renders versatile and affordable technological system. Besides, the accuracy with which the soft computing technique predicts the parameters has transformed the industrial productivity to a whole new level. The interest of this article focuses on versatile applications of SC methods to forecast the technological changes which intend to reorient the progress of various industries, and this is ascertained by a patent landscape analysis. The patent landscape revealed the players who are in the segment consistently and this also provides how this field moves on in the future and who could be a dominant country for a specific technology. Alongside, the accuracy of the soft computing method for a particular practice has also been mentioned indicating the feasibility of the technique. The novel part of this article lies in patent landscape analysis compared with the other data while the other part is the discussion of application of computational techniques to various industrial practices. The progress of various engineering applications integrating them with the patent landscape analysis must be envisaged for a better understanding of the future of all these applications resulting in an improved productivity.
... Elephant Herding Optimization (Wang, Deb, & Coelho, 2015) terinspirasi dari perilaku penggembalaan gajah dalam mencari sumber makanan dan air di alam liar. Para gajah dalam berkelompok menggunakan pencarian kolaboratif dan strategi adaptif dalam mencari sumber daya. ...
Asymmetric Traveling Salesman Problem (ATSP) adalah suatu permasalahan optimasi dimana terdapat seorang “salesman” yang harus mengunjungi beberapa kota dalam satu kali perjalanan. Pada ATSP, jarak yang ditempuh dari kota i ke j berbeda dengan jarak dari kota j ke i. Tujuan dari ATSP adalah meminimasi total jarak yang ditempuh oleh “salesman”. Pada penelitian ini Lightning Search Algorithm (LSA) dan 2-Opt local search algorithm digunakan untuk menemukan solusi dari ATSP. LSA adalah sebuah algoritma metaheuristik yang terinspirasi dari proses perambatan lidah petir (step leader) ke permukaan bumi. 2-Opt merupakan algoritma local search yang dapat memanipulasi rute agar menghasilkan solusi yang lebih baik. Penelitian ini bertujuan untuk merancang LSA dengan 2-Opt dalam menyelesaikan ATSP dan menemukan parameter yang berpengaruh terhadap solusi ATSP. Tiga parameter digunakan dalam penelitian ini, yaitu maximum channel time (max_ctime) dan forking probability (fork_prob) yang bertanggungjawab atas terjadinya fenomena forking, dan Boundaries (Bound) yang mendefinisikan ruang solusi. Pengujian ANOVA dilakukan terhadap 8 kombinasi parameter yang diimplementasi ke 5 kasus ATSP dari TSPLIB, yaitu kasus BR17, FTV33, FTV44, FTV55, dan FTV70 untuk menentukan nilai parameter terbaik. Hasil ANOVA menunjukkan parameter Bound berpengaruh terhadap solusi pada kasus FTV33, FTV44, dan FTV70. Sedangkan parameter max_ctime berpengaruh terhadap solusi pada kasus FTV55. Berdasarkan nilai parameter yang ditentukan, LSA dengan 2-Opt diimplementasikan kembali ke 5 (lima) kasus ATSP. Hasil yang didapat adalah LSA dengan 2-Opt mampu menemukan best known solution untuk kasus BR17, tetapi tidak mampu menemukan best known solution untuk kasus lain-nya.
... Phase equilibrium in non-reactive systems [170] Elephant Herding Optimization [26,[171][172][173] Integration of Lévy Flights improves the global exploration capability and avoids premature convergence. ...
Metaheuristic algorithms (MHAs) occupy considerable attention among researchers because of their high performance and robustness in optimizing several engineering problems. Random walk (RW) techniques showed a significant role in improving the performance of these algorithms. Therefore, this paper aims to provide a systematic and comprehensive review of the role of three substantial random-walk (RW) strategies in enhancing the performance of MHAs. These strategies are the Gaussian, Levy Flight and Quantum random walks. The PRISMA methodology is applied through the articles obtained from four famous scientific databases. The study provides the integration mechanisms as well as the adjusting parameters’ values of these RW strategies into Particle Swarm Optimization (PSO) to produce the Gaussian PSO (GPSO), Levy Flight PSO (LFPSO) and Quantum PSO (QPSO). An experimental study has been conducted to assess the performances of these algorithms in addition to the standard PSO on 23 unimodal, multimodal and fixed-dimension multimodal benchmark functions. Statistical measures have been calculated based on 30-run optimization processes. The comparisons showed that the QPSO, LFPSO, GPSO and PSO have successfully reached the optimal values of 23 standard benchmark functions with average percentages of 65%, 31%, 13% and 11%, respectively. Accordingly, the QPSO has gained the outstanding rank, especially for unimodal and multimodal functions followed by the LFPSO while the standard PSO comes in the last position preceded by the GPSO. From the results, it can be concluded that integrating random walk strategies into existing or new metaheuristic algorithms is capable of enhancing the optimization process and hence provides reliable results when applied to engineering applications.
... The EHOA was developed in 2015 by Wang et al. [35]. The algorithm derived its inspiration from the natural social behaviours exhibited by elephant herds. ...
In wireless environments, multi-carrier modulation (MCM) schemes provides resistance against fading. These schemes have been thoroughly researched for use in 4G/5G wireless communications because of their benefits. Wireless communication systems that use multiple carriers are the most prevalent in modern technology for high-speed transmissions of data. Many researchers are currently interested in implementing new protocols and physical layers for Filter Bank Multicarrier (FBMC) with Offset Quadrature Amplitude Modulation (OQAM). 5G transmission systems are likely to utilize the FBMC/OQAM scheme. The FBMC/OQAM system has many advantages over Orthogonal Frequency Division Multiplexing (OFDM), but there are few disadvantages, one of which is its high PAPR. Because of the signal's overlapping nature in the FBMC system, conventional reduction techniques can't be applied to the subcarriers. High peak power also reduces the efficiency of FBMC/OQAM. It is essential to reduce as much as possible the peak power of a signal in communication systems. In this article, to minimize the peak-to-average power ratio (PAPR), a Discrete Elephant Herding Optimization Algorithm (DEHOA) is used. Using the proposed method, we reduce the drawback of high PAPR with lower amalgamations of optimum phase factors for each overlapping information symbol. According to simulation results, the proposed method reduces PAPR, BER and improves spectral efficiency (SE) performance.
... The past 10 years have seen increased interest in a novel swarm-based meta-heuristic search technique dubbed elephant herding optimization that replicates the behavior of elephant groups. In the wild, female matriarchs are in charge of the herds of elephants, but when male elephants mature, they Wang et al.'s (2015) first proposal for this approach was tested on a number of benchmark functions. In essence, it is an optimization problem-solving meta-heuristic search technique based on swarms. ...
Chronic kidney disease (CKD) is a medical condition characterized by impaired kidney function, which leads to inadequate blood filtration. To reduce mortality rates, recent advancements in early diagnosis and treatment have been made. However, as diagnosis is time-consuming, an automated system is necessary. Researchers have been employing various machine learning approaches to analyze extensive and complex medical data, aiding clinicians in predicting CKD and enabling early intervention. Identifying the most crucial attributes for CKD diagnosis is this paper’s primary objective. To address this gap, six nature-inspired algorithms and nine machine learning classifiers were compared to evaluate their combined effectiveness in detecting CKD. A benchmark CKD dataset from the UCI repository was utilized for this analysis. The proposed model outperforms other classifiers with a remarkable 99.5% accuracy rate; it also achieves a 58% reduction in feature dimensionality. By providing a reliable, cost-effective tool for early CKD detection, the authors aim to revolutionize patient care.
... This behavior is mathematically modeled for the above algorithm. In 2015, a new meta-heuristics-based swarm inspired by the behavior of an elephant herd was introduced by Wang et al. [40] in the elephant herding optimization (EHO), elephants belonging to a tribe led by the head of the family live together. On the other hand, the male elephants leave their family after growing up, so the proposed algorithm uses two clans updating and separating operators for modeling. ...
The proposed paper introduces a new optimization algorithm inspired by nature called the polar fox optimization algorithm (PFA). This algorithm addresses the herd life of polar foxes and especially their hunting method. The polar fox jumping strategy for hunting, which is performed through high hearing power, is mathematically formulated and implemented to perform optimization processes in a wide range of search spaces. The performance of the polar fox algorithm is tested with 14 classic benchmark functions. To provide a comprehensive comparison, all 14 test functions are expanded, shifted, rotated and combined for this test. For further testing, the recent CEC 2021 test’s complex functions are studied in the unimodal, basic, hybrid and composition modes. Finally, the rate of convergence and computational time of PFA are also evaluated by several changes with other algorithms. Comparisons show that PFA has numerous benefits over other well-known meta-heuristic algorithms and determines the solutions with fewer control parameters. So it offers competitive and promising results. In addition, this research tests PFA performance with 6 different challenging engineering problems. Compared to the well-known meta-artist methods, the superiority of the PFA is observed from the experimental results of the proposed algorithm in real-world problem-solving. The source codes of the PFA are publicly available at https://github.com/ATR616/PFA.
... The movement aims to explore and exploit the solution space, seeking optimal solutions [38]. The algorithm iteratively refines the positions of elephants until convergence, mimicking the cooperative behavior observed in herding elephants [39]. The mathematical formulation emphasizes the balance between individual exploration and the influence of the herd, contributing to the algorithm's global exploration and exploitation capabilities [37]. ...
Support Vector Machines (SVMs) have gained prominence in machine learning for their capability to establish optimal decision boundaries in high-dimensional spaces. SVMs are powerful machine learning models but can encounter difficulties in achieving optimal performance due to challenges such as selecting appropriate kernel parameters, handling uncertain data, and adapting to complex decision boundaries.. This paper introduces a novel hybrid approach to enhance the performance of Support Vector Machines (SVM) through the integration of the Davidon-Fletcher-Powell (DFP) optimization algorithm and Elephant Herding Optimization (EHO) for parameter tuning. SVM, a robust machine learning algorithm, relies on effective hyperparameter selection for optimal performance. The proposed hybrid model synergistically leverages DFP's efficiency in unconstrained optimization and EHO's exploration-exploitation balance inspired by elephant herding behavior. The fusion of these algorithms address the challenges associated with traditional optimization methods. The hybrid model offers improved convergence towards the global optimum. Experimental results demonstrate the efficacy of the approach, showcasing enhanced SVM performance in terms of minimum 3.3% accuracy and 3.4% efficiency. This research contributes to advancing the field of metaheuristic optimization in machine learning, providing a promising avenue for effective parameter optimization in SVM applications.
Unlike their deterministic counterparts, metaheuristic optimization algorithms are practically proficient yet disreputably hard to analyze and understand. The stochastic nature lies in the metaheuristic properties that generally search for the optimized solution based on the trial-and-error attempt.
A swarm intelligence algorithm usually iterates many times to approximate the optimum to obtain the solution of a problem. The maximum iteration is influenced by many factors such as the algorithm itself, problem types, as well as dimensions and search space sizes of decision variables. There are few existing studies on efficient maximum iterations, especially a large-scale study on comparison for different problem types. By dividing three CEC benchmark sets into several problem types, this study made a large-scale performance comparison of 123 common swarm intelligence algorithms from several views. The experimental results show that for low-dimensionality, wide search space, and/or simple- and medium-complex problems, about a quarter of the algorithms are concentrated in iterations of about 30 ~ 80, while most algorithms for other types of problems tend to have as many iterations as possible. By and large, for the Classical set, large iterations are beneficial for improving the performance of most algorithms, while less than half of the algorithms for CEC 2019 and CEC 2022 do so. And, the efficient iterations of excellent algorithms are about 300 on low dimensionality, wide search space and simple-complexity problems, while other types are as large as possible. In terms of algorithm speed, LSO, DE and RSA are the fastest on all the three benchmark sets, and the runtime of all algorithms is almost linearly related to the maximum iterations. Although the conclusions largely depend on the problem types, we believe that an efficient iteration is necessary to optimize algorithm performance.
The study of animal behavior has always been a subject of interest, dating back to prehistoric times when humans had to understand the habits of species to protect themselves or enhance their hunting strategies. Nowadays, understanding animal behavior remains a concern, especially in the current era where ecological and economic issues related to species of interest weigh heavily on our society. In a context where spatial, temporal, technical, and financial constraints can hinder direct study of animal behavior, computer modeling and simulation have greatly contributed to overcoming these limitations. Modeling simulations offer the possibility to predict the behavior of specific species and test various scenarios, facilitating decision-making. The rapid evolution of modeling techniques, particularly with the advent of Deep Learning, has significantly enhanced the efficiency of behavior models. However, these models often have a major drawback: the more accurate they are, the less interpretable and explainable they become. Moreover, their implementation by non-experts in computer science, such as biologists, can be laborious.
To address this issue, this thesis proposes an innovative approach treating animal behavior modeling as an optimization problem. This method relies on a database of elementary actions in which one must find optimal actions and parameters that best reproduce the observed behavior described in previously collected data using technological means such as sensors or videos. The resolution of such optimization problems has been done with metaheuristics, a class of resolution methods particularly effective for this type of problem. Thus, we proposed and developed the ANIMETA approach, which integrates a set of tools contributing to the generation of animal behavior models that are both accurate, interpretable, and explainable.
The developed ANIMETA system consists of ANIMETA-MOD, a prototype model designed to represent animal behavior through elementary actions. To be simulated, it has been integrated into a multi-agent system called ANIMETA-SMA, designed for this purpose. The ANIMETA-ENGINE tool, responsible for the actual generation of models, uses metaheuristics to select optimal actions and parameters. The interface between ANIMETA-ENGINE and the user, as well as other computer systems, is ensured by the tools ANIMETA-HIM and ANIMETA-API. ANIMETA-HIM was specifically designed to be simple, streamlined, and intuitive for users. The approach and tools developed for this purpose were verified and validated with four different models, namely two experimental models, a pig behavior model, and a model generated from direct observations of Sciaena umbra. The results from the test series undergone by these models show the concordance of ANIMETA with other platforms and demonstrate its speed compared to other methods. However, some points still need improvement, particularly the optimization duration that could be reduced by exploring possibilities for parallelization and modifying the solution evaluation process. Despite these few suboptimal performances, ANIMETA still offers encouraging results for animal behavior modeling. Therefore, our perspectives suggest improving certain algorithms, adding dedicated evaluation functions, and expanding the database of elementary actions through a collaborative platform.
Integrating new generation and storage resources within power systems is challenging because of the stochastic nature of renewable generation, voltage regulation, and the use of microgrids. Classical optimization methods struggle with these nonlinear, multifaceted issues. This paper presents a novel optimization framework for integrating, sizing, and siting distributed renewable generation and energy storage systems in power distribution networks. To accurately reflect load variability, the framework considers four distinct load models—constant impedance, current, power, and ZIP (constant impedance, constant current, constant power). Our approach utilized three metaheuristic approaches to enhance the efficiency of power system management. The validation results on the IEEE 33 Bus System conclude that the Elephant Herding Optimization (EHO) emerged as the best performer regarding voltage stability and real power loss reduction with a voltage stability index of 0.0031346. Modified Ant Lion Optimization (ALO) achieved a best voltage stability index of 0.0024115 and power losses of 7.5092 MVA. The Red Colobus Monkey Optimization (RMO) algorithm realized a voltage stability index of 0.0052053 and real power losses of 20.7564 MVA. Overall, the results conclude that ALO is the most effective approach for optimizing distributed renewable energy systems under different climatic conditions. According to the analysis, the algorithm works best in ideal circumstances when the percentages of wind and irradiance are 60% or greater.
Accurate and early detection of brain tumors is essential for improving clinical outcomes and guiding effective treatment planning. Traditional segmentation techniques in MRI often struggle with challenges such as noise, intensity variations, and complex tumor morphologies, which can hinder their effectiveness in critical healthcare scenarios. This study proposes an innovative hybrid methodology that integrates advanced metaheuristic optimization and entropy-based fuzzy clustering to enhance segmentation precision in brain tumor detection. This method combines the nature-inspired Elephant Herding Optimization (EHO) algorithm with Entropy-Driven Fuzzy C-Means (EnFCM) clustering, offering significant improvements over conventional methods. EHO is utilized to optimize the clustering process, enhancing the algorithm’s ability to delineate tumor boundaries, while entropy-based fuzzy clustering accounts for intensity inhomogeneity and diverse tumor characteristics, promoting more consistent and reliable segmentation results. This approach was evaluated using the BraTS challenge dataset, a benchmark in the field of brain tumor segmentation. The results demonstrate marked improvements across several performance metrics, including Dice similarity, mean squared error (MSE), peak signal-to-noise ratio (PSNR), and the Tanimoto coefficient (TC), underscoring this method’s robustness and segmentation accuracy. By managing image noise and reducing computational demands, the EHO-EnFCM approach not only captures intricate tumor structures but also facilitates efficient image processing, making it suitable for real-time clinical applications. Overall, the findings reveal the potential of this hybrid approach to advance MRI-based tumor detection, offering a promising tool that enhances both accuracy and computational efficiency for medical imaging and diagnosis.
Bio-inspired optimization algorithms use natural processes and biological phenomena as a basis for solving difficult optimization issues. This article discusses state-of-the-art techniques, applications, and implementations of eleven well-known bio-inspired optimization algorithms: Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Artificial Bee Colony Algorithm (ABC), Grey Wolf Optimizer (GWO), Firefly Algorithm (FA), Shuffled Frog Learning Algorithm (SFLA), Elephant Herd Optimizer (EHO), Lion Optimization Algorithm (LOA), Genetic Algorithm (GA), Flower Pollination Algorithm (FPA) and Bat Algorithm (BAT). Accordingly, each algorithm is considered in terms of the biological principles from which it is modelled, key mechanisms in operation, and the mathematical treatment. The current article also gives an account of recent improvements and modifications of these algorithms, made in an attempt to enhance their performance, speed of convergence, and robustness along with various real-world applications.
The Growth Optimizer algorithm (GO) is a novel metaheuristic that draws inspiration from people's learning and introspection processes as they progress through society. Economic Load Dispatch (ELD), one of the primary problems in the power system, is resolved by the GO. To assess GO's dependability, its performance is contrasted with a number of methods. These techniques include the Rime-ice algorithm (RIME), Grey Wolf Optimizer (GWO), Elephant Herding Optimization (EHO), and Tunicate Swarm Algorithm (TSA). Also, the GO algorithm has the competition of other literature techniques such as Monarch butterfly optimization (MBO), the Sine Cosine algorithm (SCA), the chimp optimization algorithm (ChOA), the moth search algorithm (MSA), and the snow ablation algorithm (SAO). Six units for the ELD problem at a 1000 MW load, ten units for the ELD problem at a 2000 MW load, and twenty units for the ELD problem at a 3000 MW load are the cases employed in this work. The standard deviation, minimum fitness function, and maximum mean values are measured for 30 different runs in order to evaluate all methods. Using the GO approach, the ideal power mismatch values of 3.82627263206814 × 10 −12 , 0.0000622209480241054, and 5.5893360695336 × 10 −7 were found for six, ten, and twenty generator units, respectively. The GO's dominance over all other algorithms is demonstrated by the results produced for the ELD scenarios.
This chapter dedicates the essential principle and improvement options for metaheuristic algorithms during the initialization of the optimization search. All metaheuristic algorithms must follow the initialization procedure before the primary optimization search process, as highlighted in Fig. 1.2. As also understood among algorithm developers and researchers, good tradeoffs between exploration and exploitation mechanisms are fundamental in metaheuristic algorithms to ensure a good optimization process concerning solution quality and faster convergence. The initialization phase is one of the main factors contributing to a better exploration–exploitation balancing mechanism. This chapter discusses the initialization phase of metaheuristic algorithms and development options proposed in numerous pieces of literature to improve the algorithm later in the optimization search. The outline of this chapter is summarized in Fig. 1.1.
This chapter discusses further possibilities of exploration and exploitation mechanisms during the optimization search. It can be regarded as the conjunction of the previous chapter but mainly focuses on distributions and functions.
Optimizasyon, tüm olası alternatifler arasından bir problemin en optimal çözümünü belirleme sürecidir. Enerji sistemlerinde metasezgisel optimizasyon algoritmaları, karmaşık enerji problemlerini çözmede önemli bir rol oynamaktadır. Metasezgisel optimizasyon algoritmaları, genetik algoritmalar, parçacık sürü optimizasyonu, simüle edilen tavlama, karınca kolonisi optimizasyonu gibi doğal süreçlerden esinlenerek geliştirilen ve genellikle bilgisayar tabanlı modellerle kullanılan özel optimizasyon yöntemleridir. Metasezgisel optimizasyon algoritmaları, büyük veri setleriyle çalışabilir ve farklı kısıtlamalar altında optimize edilmesi gereken çok sayıda değişkeni ele alabilirler. Bu nedenle enerji sektöründe sürdürülebilirlik, verimlilik ve karlılık açısından büyük öneme sahiptirler. Bu algoritmalar, enerji verimliliğini artırmak, enerji maliyetini azaltmak, enerji üretimi, dağıtımı, tüketimi ve depolanması gibi enerji sistemlerinin farklı bileşenlerini optimize etmek için, yenilenebilir enerji kaynaklarını entegre etmek ve enerji sistemlerinin karbon ayak izini azaltmak gibi çeşitli hedeflere ulaşmak için kullanılmaktadırlar. Bu çalışmada, enerji sistemleri uygulamalarında metasezgisel optimizasyon algoritmalarının kullanımı örnekler üzerinden incelenmiştir. Bu algoritmaların kullanımı ile karmaşık problemlerin çözümlerinin daha kolaya indirgendiği görülmüştür.
Nature-inspired methods are finding more and more practical applications. At present, one can observe a strong development of these techniques associated with the design of new algorithms or new modifications of currently known algorithms. Among the whole family of nature-based optimization algorithms, swarm intelligence algorithms take a special place. In these algorithms, the main focus is on cooperation between individual solutions, which results in the broader perspective of the algorithm to reach better and better solutions. This chapter presents a short overview of swarm intelligence algorithms (SIAs). The basic algorithm derived from this family, which is the particle swarm optimization algorithm, is shown in detail. The chapter presents the popularity of SIAs based on publicly available scientific databases such as Springer, Google Scholar, Science Direct, IEEE Explore and Web of Science. The main areas in which the previously indicated algorithms are applied are also presented, and several papers related to engineering applications of swarm intelligence algorithms are briefly discussed. The chapter concludes with a summary.
The sand cat swarm optimization (SCSO) is a recently proposed meta-heuristic algorithm. It inspires hunting behavior with sand cats based on hearing ability. However, in the later stage of SCSO, it is easy to fall into local optimality and cannot find a better position. In order to improve the search ability of SCSO and avoid falling into local optimal, an improved algorithm is proposed - Improved sand cat swarm optimization based on lens opposition-based learning and sparrow search algorithm (LSSCSO). A dynamic spiral search is introduced in the exploitation stage to make the algorithm search for better positions in the search space and improve the convergence accuracy of the algorithm. The lens opposition-based learning and the sparrow search algorithm are introduced in the later stages of the algorithm to make the algorithm jump out of the local optimum and improve the global search capability of the algorithm. To verify the effectiveness of LSSCSO in solving global optimization problems, CEC2005 and CEC2022 test functions are used to test the optimization performance of LSSCSO in different dimensions. The data results, convergence curve and Wilcoxon rank sum test are analyzed, and the results show that it has a strong optimization ability and can reach the optimal in most cases. Finally, LSSCSO is used to verify the effectiveness of the algorithm in solving engineering optimization problems.
Gene expression in the microarray is assimilated with redundant and high-dimensional information. Moreover, the information in the microarray genes mostly correlates with background noise. This paper uses dimensionality reduction and feature selection methods to employ a classification methodology for high-dimensional lung cancer microarray data. The approach is enforced in two phases; initially, the genes are dimensionally reduced through Hilbert Transform, Detrend Fluctuation Analysis and Least Square Linear Regression methods. The dimensionally reduced data is further optimized in the next phase using Elephant Herd optimization (EHO) and Cuckoo Search Feature selection methods. The classifiers used here are Bayesian Linear Discriminant, Naive Bayes, Random Forest, Decision Tree, SVM (Linear), SVM (Polynomial), and SVM (RBF). The classifier's performances are analysed with and without feature selection methods. The SVM (Linear) classifier with the DFA Dimensionality Reduction method and EHO feature selection achieved the highest accuracy of 92.26 % compared to other classifiers.
The most fatal disease on the earth is thought to be illness of the heart. There are a lot of features that change the heart’s composition or functionality. In most cases, it is hard for doctors to make a diagnosis accurately and quickly. This study’s objective is to determine critical factors and methods of data mining which can improve the accuracy for prediction of heart disease. Further, it is essential to make use of automatic technologies in diagnosing heart diseases as early as possible. To develop a new prediction technique for heart disease that comprises four phases such as “(a) Pre-processing, (b) Feature extraction, (c) Feature selection and (d) Classification”. The initial stage of pre-processing is when the incoming data is treated to the elimination of redundant and missed numbers. Then, from the initial stage of data, the higher-order statistical and statistical characteristics, chi-squared features and symmetrical uncertainty attributes are derived. However, when working with a greater number of characteristics, the curse of dimensionality was a severe issue. Hence, the characteristics of optimal features are planned from the overall set of features. A novel Hybrid Bull and Elephant Algorithm (HB-EA) is introduced for the selection of optimal features. Consequently, the selected set of features is subjected to various classifiers as an ensemble model that contains “Naïve Bayes (NB), Decision Tree (DT), Neural Network (NN), Support Vector Machine (SVM), Optimized Recurrent Neural Network (RNN) and Linear Regression (LR)”. The final step is to log off efficiency for outputs obtained from the group of classifiers and determine the outcome. The RNN weights are ideally tuned by the suggested HB-EA technique to boost the system’s accuracy. The proposed model is finally evaluated against existing techniques to determine its superiority. The suggested technique for dataset 1 achieved maximum accuracy (0.916), and it is 15.24%, 8.76%, 7.56%, 4.09% and 1.89% better than convolution schemes like Random Forest (RF), Deep Belief Network (DBN), SVM, [Formula: see text]-Nearest Neighbor (KNN) and Elephant Herding Optimization (EHO) models.
Nature, with its numerous surprising rules, serves as a rich source of creativity for the development of artificial intelligence, inspiring researchers to create several nature-inspired intelligent computing paradigms based on natural mechanisms. Over the past decades, these paradigms have revealed effective and flexible solutions to practical and complex problems. This paper summarizes the natural mechanisms of diverse advanced nature-inspired intelligent computing paradigms, which provide valuable lessons for building general-purpose machines capable of adapting to the environment autonomously. According to the natural mechanisms, we classify nature-inspired intelligent computing paradigms into 4 types: evolutionary-based, biological-based, social-cultural-based, and science-based. Moreover, this paper also illustrates the interrelationship between these paradigms and natural mechanisms, as well as their real-world applications, offering a comprehensive algorithmic foundation for mitigating unreasonable metaphors. Finally, based on the detailed analysis of natural mechanisms, the challenges of current nature-inspired paradigms and promising future research directions are presented.
This paper presents a new optimization algorithm that efficiently can optimize the beamforming of the linear and planar antenna arrays. The new algorithm is called the corrosion diffusion optimization algorithm (CDOA) because its concept is concluded from the behavior of the corrosion distribution on the metal surfaces. The general performance of the new algorithm is compared with some other important algorithms via some standard benchmark functions. This comparison results in about 100% success rate for the proposed algorithm with high execution speed and low complexity compared to the other algorithms. Subsequently, CDOA is applied to solve some of the antenna array beamforming problems by optimizing the amplitude of the antenna weights vector and/or the inter-element spacing between the elements of the array. The algorithm results in a side lobe ratio larger than 22 dB in solving the problem of enhancing the side lobe ratio. Besides, CDOA operates efficiently in generating a flat top and triangular beam shaping with enhanced side lobe ratio. Furthermore, all the jamming signals are perfectly eliminated in the anti-jamming antenna array system when optimizing the weights vector of the array using the proposed algorithm. For each beamforming problem, the outcomes of CDOA are compared with other optimization algorithms to highlight its superiority in solving these kinds of problems.
Attaining a dependable measurement of concrete slump is crucial as it is a valuable indication of concrete workability. On the other hand, complexities associated with costly traditional approaches have driven engineers to use indirect efficient models such as metaheuristic-based machine learning for approximating the slump. While the literature shows promising application of some metaheuristic techniques for this purpose, the large variety of these algorithms calls for evaluating the most capable ones to keep the solution updated. Stochastic fractal search (SFS) is one of the most powerful optimization algorithms in the literature that has not received appropriate attention in analyzing concrete mechanical parameters. In the present research, a multi-layer perceptron neural network (NN-MLP), is enhanced using the SFS. The proposed SFS-NN-MLP model aims to predict the slump based on the amount of ingredients in the mixture, as well as the curing age of specimens. Accuracy assessment revealed that the proposed model can deal with the assigned task with excellent accuracy. It indicates that the SFS could properly tune the parameters required for training the NN-MLP, and consequently, the trained network could reliably calculate the slump of specimens that were not analyzed before. For comparative validation, the SFS was replaced with two similar optimizers, namely elephant herding optimization algorithm (EHO) and slime mould algorithm (SMA). Based on the calculated mean square errors of 5.6526, 6.1129, and 7.3561 along with mean absolute errors of 4.6657, 5.0078, and 6.3066, as well as the percentage-Pearson correlation coefficients of 78.06%, 73.95%, and 58.11%, respectively for the SFS-NN-MLP, EHO-NN-MLP, and SMA-NN-MLP, it was shown that the SFS-NN-MLP is the most accurate predictor. Hereupon, the SFS-NN-MLP model is recommended to be effectively used for obtaining a cost-efficient approximation of concrete slump in real-world projects.
Prostate cancer is a major world health problem for men. This shows how important early detection and accurate diagnosis are for better treatment and patient outcomes. This study compares different ways to find Prostate Cancer (PCa) and label tumors as normal or abnormal, with the goal of speeding up current work in microarray gene data analysis. The study looks at how well several feature extraction methods work with three feature selection strategies: Harmonic Search (HS), Firefly Algorithm (FA), and Elephant Herding Optimization (EHO). The techniques tested are Expectation Maximization (EM), Nonlinear Regression (NLR), K-means, Principal Component Analysis (PCA), and Discrete Cosine Transform (DCT). Eight classifiers are used for the task of classification. These are Random Forest, Decision Tree, Adaboost, XGBoost, and Support Vector Machine (SVM) with linear, polynomial, and radial basis function kernels. This study looks at how well these classifiers work with and without feature selection methods. It finds that the SVM with radial basis function kernel, using DCT for feature extraction and EHO for feature selection, does the best of all of them, with an accuracy of 94.8 % and an error rate of 5.15 %.
Recently, Gandomi and Alavi proposed a new heuristic search method, called krill herd (KH), for solving global optimisation problems. In order to make KH more effective, a hybrid meta-heuristic cuckoo search and krill herd (CSKH) method is proposed for function optimisation. The CSKH introduces krill updating (KU) and krill abandoning (KA) operator originated from cuckoo search (CS) during the process when the krill updating so as to greatly enhance its effectiveness and reliability dealing with numerical optimisation problems. The KU operator inspires the intensive exploitation and allows the krill individuals implement a careful search in the later run phase of the search, while KA operator is used to further enhance the exploration of the CSKH in place of a fraction of the worse krill at the end of each generation. The effectiveness of these improvements is tested by 14 standard benchmarking functions and experimental results show, in most cases, this hybrid meta-heuristic CSKH algorithm is more effective and efficient than the original KH and other approaches.
The Big Bang-Big Crunch (BB-BC) optimization algorithm is a new optimization method that relies on the Big Bang and Big Crunch theory, one of the theories of the evolution of the universe. In this paper, a discrete Big Bang-Big Crunch algorithm is presented for optimal design of structures. The BB-BC is employed to optimize different types of skeletal structures with discrete variables including trusses and frames. The results demonstrate the efficiency of the discrete BB-BC algorithm compared to other heuristic algorithms.
This study proposes a novel chaotic cuckoo search (CCS) optimization method by incorporating chaotic theory into cuckoo search (CS) algorithm. In CCS, chaos characteristics are combined with the CS with the intention of further enhancing its performance. Further, the elitism scheme is incorporated into CCS to preserve the best cuckoos. In CCS method, 12 chaotic maps are applied to tune the step size of the cuckoos used in the original CS method. Twenty-seven benchmark functions and an engineering case are utilized to investigate the efficiency of CCS. The results clearly demonstrate that the performance of CCS together with a suitable chaotic map is comparable as well as superior to that of the CS and other metaheuristic algorithms.
This paper proposes a novel nature-inspired algorithm called Multi-Verse Optimizer (MVO). The main inspirations of this algorithm are based on three concepts in cosmology: white hole, black hole, and wormhole. The mathematical models of these three concepts are developed to perform exploration, exploitation, and local search, respectively. The MVO algorithm is first benchmarked on 19 challenging test problems. It is then applied to five real engineering problems to further confirm its performance. To validate the results, MVO is compared with four well-known algorithms: Grey Wolf Optimizer, Particle Swarm Optimization, Genetic Algorithm, and Gravitational Search Algorithm. The results prove that the proposed algorithm is able to provide very competitive results and outperforms the best algorithms in the literature on the majority of the test beds. The results of the real case studies also demonstrate the potential of MVO in solving real problems with unknown search spaces. Note that the source codes of the proposed MVO algorithm are publicly available at http://www.alimirjalili.com/MVO.html.
The biogeography-based optimisation (BBO) algorithm is a novel evolutionary algorithm inspired by biogeography. Similarly, to other evolutionary algorithms, entrapment in local optima and slow convergence speed are two probable problems it encounters in solving challenging real problems. Due to the novelty of this algorithm, however, there is little in the literature regarding alleviating these two problems. Chaotic maps are one of the best methods to improve the performance of evolutionary algorithms in terms of both local optima avoidance and convergence speed. In this study, we utilise ten chaotic maps to enhance the performance of the BBO algorithm. The chaotic maps are employed to define selection, emigration, and mutation probabilities. The proposed chaotic BBO algorithms are benchmarked on ten test functions. The results demonstrate that the chaotic maps (especially Gauss/mouse map) are able to significantly boost the performance of BBO. In addition, the results show that the combination of chaotic selection and emigration operators results in the highest performance.
The differential evolution algorithm (DE) is a simple and effective global optimization algorithm. It has been successfully applied to solve a wide range of real-world optimization problem. In this paper, the proposed algorithm uses two mutation rules based on the rand and best individuals among the entire population. In order to balance the exploitation and exploration of the algorithm, two new rules are combined through a probability rule. Then, self-adaptive parameter setting is introduced as uniformly random numbers to enhance the diversity of the population based on the relative success number of the proposed two new parameters in a previous period. In other aspects, our algorithm has a very simple structure and thus it is easy to implement. To verify the performance of MDE, 16 benchmark functions chosen from literature are employed. The results show that the proposed MDE algorithm clearly outperforms the standard differential evolution algorithm with six different parameter settings. Compared with some evolution algorithms (ODE, OXDE, SaDE, JADE, jDE, CoDE, CLPSO, CMA-ES, GL-25, AFEP, MSAEP and ENAEP) from literature, experimental results indicate that the proposed algorithm performs better than, or at least comparable to state-of-the-art approaches from literature when considering the quality of the solution obtained.
Gene expression data play an important role in the development of efficient cancer diagnoses and classification. However, gene expression data are usually redundant and noisy, and only a subset of them present distinct profiles for different classes of samples. Thus, selecting high discriminative genes from gene expression data has become increasingly interesting in the field of bioinformatics. In this paper, a multi-objective biogeography based optimization method is proposed to select the small subset of informative gene relevant to the classification. In the proposed algorithm, firstly, the Fisher-Markov selector is used to choose the 60 top gene expression data. Secondly, to make biogeography based optimization suitable for the discrete problem, binary biogeography based optimization, as called BBBO, is proposed based on a binary migration model and a binary mutation model. Then, multi-objective binary biogeography based optimization, as we called MOBBBO, is proposed by integrating the non-dominated sorting method and the crowding distance method into the BBBO framework. Finally, the MOBBBO method is used for gene selection, and support vector machine is used as the classifier with the leave-one-out cross-validation method (LOOCV). In order to show the effective and efficiency of the algorithm, the proposed algorithm is tested on ten gene expression dataset benchmarks. Experimental results demonstrate that the proposed method is better or at least comparable with previous particle swarm optimization (PSO) algorithm and support vector machine (SVM) from literature when considering the quality of the solutions obtained.
This work proposes a new meta-heuristic called Grey Wolf Optimizer (GWO) inspired by grey wolves (Canis lupus). The GWO algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. Four types of grey wolves such as alpha, beta, delta, and omega are employed for simulating the leadership hierarchy. In addition, the three main steps of hunting, searching for prey, encircling prey, and attacking prey, are implemented. The algorithm is then benchmarked on 29 well-known test functions, and the results are verified by a comparative study with Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Differential Evolution (DE), Evolutionary Programming (EP), and Evolution Strategy (ES). The results show that the GWO algorithm is able to provide very competitive results compared to these well-known meta-heuristics. The paper also considers solving three classical engineering design problems (tension/compression spring, welded beam, and pressure vessel designs) and presents a real application of the proposed method in the field of optical engineering. The results of the classical engineering design problems and real application prove that the proposed algorithm is applicable to challenging problems with unknown search spaces.
A hybrid metaheuristic approach by hybridizing harmony search (HS) and firefly algorithm (FA), namely, HS/FA, is proposed to solve function optimization. In HS/FA, the exploration of HS and the exploitation of FA are fully exerted, so HS/FA has a faster convergence speed than HS and FA. Also, top fireflies scheme is introduced to reduce running time, and HS is utilized to mutate between fireflies when updating fireflies. The HS/FA method is verified by various benchmarks. From the experiments, the implementation of HS/FA is better than the standard FA and other eight optimization methods.
The cuckoo search algorithm is a simple and effective global optimization algorithm. It has been successfully applied to solve a wide range of real-world optimization problem. In this paper, we use a new search strategy based on orthogonal learning strategy to enhance the exploitation ability of the basic cuckoo search algorithm. In order to verify the performance of our approach, 23 benchmark functions are employed. Experimental results indicate that the proposed algorithm performs better than or at least comparable to state-of-the-art approaches from literature when considering the quality of the solution obtained.
In this paper, we intend to propose a new heuristic optimization method, called animal migration optimization algorithm. This algorithm is inspired by the animal migration behavior, which is a ubiquitous phenomenon that can be found in all major animal groups, such as birds, mammals, fish, reptiles, amphibians, insects, and crustaceans. In our algorithm, there are mainly two processes. In the first process, the algorithm simulates how the groups of animals move from the current position to the new position. During this process, each individual should obey three main rules. In the latter process, the algorithm simulates how some animals leave the group and some join the group during the migration. In order to verify the performance of our approach, 23 benchmark functions are employed. The proposed method has been compared with other well-known heuristic search methods. Experimental results indicate that the proposed algorithm performs better than or at least comparable with state-of-the-art approaches from literature when considering the quality of the solution obtained.
A new heuristic approach for minimizing possiblynonlinear and non-differentiable continuous spacefunctions is presented. By means of an extensivetestbed it is demonstrated that the new methodconverges faster and with more certainty than manyother acclaimed global optimization methods. The newmethod requires few control variables, is robust, easyto use, and lends itself very well to parallelcomputation.
This paper presents a ranked differential evolution (RDE) algorithm for solving the identification problem of non-linear discrete-time systems based on a Volterra filter model. In the improved method, a scale factor, generated by combining a sine function and randomness, effectively keeps a balance between the global search and the local search. Also, the mutation operation is modified after ranking all candidate solutions of the population to help avoid the occurrence of premature convergence. Finally, two examples including a highly nonlinear discrete-time rational system and a real heat exchanger are used to evaluate the performance of the RDE algorithm and five other approaches. Numerical experiments and comparisons demonstrate that the RDE algorithm performs better than the other approaches in most cases.
In order to fully exploit global search and local search ability of krill herd (KH), a multi-stage krill herd (MSKH) algorithm is presented to solve global numerical optimization problem. The proposed method involves exploration and exploitation stage. The exploration stage uses a basic krill herd (KH) algorithm to select a good candidate solution set. It is followed by fine-tuning a good candidate solution in the exploitation stage with a focused local mutation and crossover (LMC) operator in order to enhance its efficiency and reliability when solving global numerical optimization problems. The elitism scheme is also applied to make MSKH method move towards best solution all the time. The effectiveness of the MSKH is demonstrated by twenty-five standard and rotated and shifted benchmark problems. The results clearly demonstrate the efficiency of the proposed MSKH algorithm as compared to the standard version of the KH and other eight optimization methods.
A novel swarm intelligence optimization technique is proposed called dragonfly algorithm (DA). The main inspiration of the DA algorithm originates from the static and dynamic swarming behaviours of dragonflies in nature. Two essential phases of optimization, exploration and exploitation, are designed by modelling the social interaction of dragonflies in navigating, searching for foods, and avoiding enemies when swarming dynamically or statistically. The paper also considers the proposal of binary and multi-objective versions of DA called binary DA (BDA) and multi-objective DA (MODA), respectively. The proposed algorithms are benchmarked by several mathematical test functions and one real case study qualitatively and quantitatively. The results of DA and BDA prove that the proposed algorithms are able to improve the initial random population for a given problem, converge towards the global optimum, and provide very competitive results compared to other well-known algorithms in the literature. The results of MODA also show that this algorithm tends to find very accurate approximations of Pareto optimal solutions with high uniform distribution for multi-objective problems. The set of designs obtained for the submarine propeller design problem demonstrate the merits of MODA in solving challenging real problems with unknown true Pareto optimal front as well. Note that the source codes of the DA, BDA, and MODA algorithms are publicly available at http://www.alimirjalili.com/DA.html.
Evolutionary population dynamics (EPD) deal with the removal of poor individuals in nature. It has been proven that this operator is able to improve the median fitness of the whole population, a very effective and cheap method for improving the performance of meta-heuristics. This paper proposes the use of EPD in the grey wolf optimizer (GWO). In fact, EPD removes the poor search agents of GWO and repositions them around alpha, beta, or delta wolves to enhance exploitation. The GWO is also required to randomly reinitialize its worst search agents around the search space by EPD to promote exploration. The proposed GWO–EPD algorithm is benchmarked on six unimodal and seven multi-modal test functions. The results are compared to the original GWO algorithm for verification. It is demonstrated that the proposed operator is able to significantly improve the performance of the GWO algorithm in terms of exploration, local optima avoidance, exploitation, local search, and convergence rate.
Purpose
– Meta-heuristic algorithms are efficient in achieving the optimal solution for engineering problems. Hybridization of different algorithms may enhance the quality of the solutions and improve the efficiency of the algorithms. The purpose of this paper is to propose a novel, robust hybrid meta-heuristic optimization approach by adding differential evolution (DE) mutation operator to the accelerated particle swarm optimization (APSO) algorithm to solve numerical optimization problems.
Design/methodology/approach
– The improvement includes the addition of DE mutation operator to the APSO updating equations so as to speed up convergence.
Findings
– A new optimization method is proposed by introducing DE-type mutation into APSO, and the hybrid algorithm is called differential evolution accelerated particle swarm optimization (DPSO). The difference between DPSO and APSO is that the mutation operator is employed to fine-tune the newly generated solution for each particle, rather than random walks used in APSO.
Originality/value
– A novel hybrid method is proposed and used to optimize 51 functions. It is compared with other methods to show its effectiveness. The effect of the DPSO parameters on convergence and performance is also studied and analyzed by detailed parameter sensitivity studies.
In nature, the eastern North American monarch population is known for its southward migration
during the late summer/autumn from the northern United States and southern Canada to Mexico,
covering thousands of miles. By simplifying and idealizing the migration of monarch butterflies, a
new kind of nature-inspired metaheuristic algorithm, called Monarch Butterfly Optimization
(MBO), a first of its kind, is proposed in this paper. In MBO, all the monarch butterfly individuals
are located in two distinct lands viz. Southern Canada & northern United States (Land 1) and
Mexico (Land 2). Accordingly, the positions of the monarch butterflies are updated in two ways.
Firstly, the offsprings are generated (position updating) by migration operator, which can be
adjusted by the migration ratio. It is followed by tuning the positions for other butterflies by
means of butterfly adjusting operator. In order to keep the population unchanged and minimize
fitness evaluations, the sum of the newly-generated butterflies in these two ways remains equal to
the original population. In order to demonstrate the superior performance of the MBO algorithm, a
comparative study with five other metaheuristic algorithms through thirty-eight benchmark
problems, is carried out. The results clearly exhibit the capability of the MBO method towards
finding the enhanced function values on most of the benchmark problems w.r.t. the other five
algorithms.
A novel hybrid Krill herd (KH) and quantum-behaved particle swarm optimization (QPSO), called KH-QPSO, is presented for benchmark and engineering optimization. QPSO is intended for enhancing the ability of the local search and increasing the individual diversity in the population. KH-QPSO is capable of avoiding the premature convergence and eventually finding the function minimum; especially, KH-QPSO can make all the individuals proceed to the true global optimum without introducing additional operators to the basic KH and QPSO algorithms. To verify its performance, various experiments are carried out on an array of test problems as well as an engineering case. Based on the results, we can easily infer that the hybrid KH-QPSO is more efficient than other optimization methods for solving standard test problems and engineering optimization problems.
By simulating the echolocation behavior of bats in nature, bat algorithm (BA) is proposed for global optimization that is a recently developed nature-inspired algorithm. Since then, it has been widely used in various fields. Bat algorithm balance the global search and local search by adjusting loudness and pulse rate. However, there is so many loudness and pulse rate combinations that it is hard to choose the most proper one for different problems. In this paper, a multi-swarm algorithm, called multi-swarm bat algorithm (MBA), is proposed for global search problem. In MBA method, immigration operator is used to exchange information between different swarms with different parameter settings. Thus, this configuration can make a good trade-off between global and local search. In addition, the best individuals of every swarm is put into the elite swarm through selection operator. The bat individuals in elite swarm pass over next generation without performing any operators, and this can ensure these best solutions cannot be damaged during optimization process. In order to evaluate the efficiency of MBA method, MBA has been benchmarked by sixteen standard test functions by comparing with basic BA. The results show that the MBA method is able to search more satisfactory function values on most benchmark problems than BA.
This paper proposes a novel nature-inspired algorithm called Ant Lion Optimizer (ALO). The ALO algorithm mimics the hunting mechanism of antlions in nature. Five main steps of hunting prey such as the random walk of ants, building traps, entrapment of ants in traps, catching preys, and re-building traps are implemented. The proposed algorithm is benchmarked in three phases. Firstly, a set of 19 mathematical functions is employed to test different characteristics of ALO. Secondly, three classical engineering problems (three-bar truss design, cantilever beam design, and gear train design) are solved by ALO. Finally, the shapes of two ship propellers are optimized by ALO as challenging constrained real problems. In the first two test phases, the ALO algorithm is compared with a variety of algorithms in the literature. The results of the test functions prove that the proposed algorithm is able to provide very competitive results in terms of improved exploration, local optima avoidance, exploitation, and convergence. The ALO algorithm also finds superior optimal designs for the majority of classical engineering problems employed, showing that this algorithm has merits in solving constrained problems with diverse search spaces. The optimal shapes obtained for the ship propellers demonstrate the applicability of the proposed algorithm in solving real problems with unknown search spaces as well. Note that the source codes of the proposed ALO algorithm are publicly available at http://www.alimirjalili.com/ALO.html.
In this paper, a novel particle swarm optimisation with wavelet mutation (PSOWM) has been applied for the optimal designs of three non-uniform circular antenna arrays with optimal side lobe level (SLL) reduction. Circular array antennas laid on x-y plane are assumed. PSOWM incorporates a new definition of swarm updating with the help of wavelet theory. Wavelet mutation enhances the PSO to explore the solution space more effectively compared to the other optimisation methods. Thus, PSOWM is apparently free from getting trapped at local optima and premature convergence. The approach is illustrated through 8-, 10-, and 12-element circular antenna arrays. Various simulation results are presented and radiation pattern performances are analysed. The simulation results show PSOWM outperforms GA (Panduro et al., 2006), PSO (Sahib et al., 2008), SA (Rattan et al., 2009), and BBO (Singh and Kamal, 2011) in the optimal design of three non-uniform circular antenna arrays by achieving much greater reduction in SLL and much more improved first null beamwidth (FNBW) and 3 dB beamwidth.
The Asian elephant has had a unique cultural association with people. Unfortunately, elephants and people have also been in conflict, resulting in the decline in elephants throughout their former range in Southern Asia. This book provides an ecological analysis of elephant human interaction and its implications for the conservation of elephants. The foraging habits of elephants and their impact on vegetation are considered, along with the interactions that occur between elephants and humans. The ecological data provide the basis for recommendations on elephant conservation and management, keeping in view the socioeconomic imperatives of the Asian region.This first comprehensive account of Asian elephant ecology will be of particular interest to conservation biologists and mammalogists.
Optimization problems can become intractable when the search space undergoes tremendous growth. Heuristic optimization methods have therefore been created that can search the very large spaces of candidate solutions. These methods, also called metaheuristics, are the general skeletons of algorithms that can be modified and extended to suit a wide range of optimization problems. Various researchers have invented a collection of metaheuristics inspired by the movements of animals and insects (e.g., firefly, cuckoos, bats and accelerated PSO) with the advantages of efficient computation and easy implementation. This paper studies a relatively new bio-inspired heuristic optimization algorithm called the Wolf Search Algorithm (WSA) that imitates the way wolves search for food and survive by avoiding their enemies. The WSA is tested quantitatively with different values of parameters and compared to other metaheuristic algorithms under a range of popular non-convex functions used as performance test problems for optimization algorithms, with superior results observed in most tests.
Bat algorithm (BA) is one of the recently proposed heuristic algorithms imitating the echolocation behavior of bats to perform global optimization. The superior performance of this algorithm has been proven among the other most well-known algorithms such as genetic algorithm (GA) and particle swarm optimization (PSO). However, the original version of this algorithm is suitable for continuous problems, so it cannot be applied to binary problems directly. In this paper, a binary version of this algorithm is proposed. A comparative study with binary PSO and GA over twenty-two benchmark functions is conducted to draw a conclusion. Furthermore, Wilcoxon's rank-sum nonparametric statistical test was carried out at 5 % significance level to judge whether the results of the proposed algorithm differ from those of the other algorithms in a statistically significant way. The results prove that the proposed binary bat algorithm (BBA) is able to significantly outperform others on majority of the benchmark functions. In addition, there is a real application of the proposed method in optical engineering called optical buffer design at the end of the paper. The results of the real application also evidence the superior performance of BBA in practice.
For the purpose of enhancing the search ability of the cuckoo search (CS) algorithm, an improved robust approach, called HS/CS, is put forward to address the optimization problems. In HS/CS method, the pitch adjustment operation in harmony search (HS) that can be considered as a mutation operator is added to the process of the cuckoo updating so as to speed up convergence. Several benchmarks are applied to verify the proposed method and it is demonstrated that, in most cases, HS/CS performs better than the standard CS and other comparative methods. The parameters used in HS/CS are also investigated by various simulations.
The PSOGSA is a novel hybrid optimization algorithm, combining strengths of both particle swarm optimization (PSO) and gravitational search algorithm (GSA). It has been proven that this algorithm outperforms both PSO and GSA in terms of improved exploration and exploitation. The original version of this algorithm is well suited for problems with continuous search space. Some problems, however, have binary parameters. This paper proposes a binary version of hybrid PSOGSA called BPSOGSA to solve these kinds of optimization problems. The paper also considers integration of adaptive values to further balance exploration and exploitation of BPSOGSA. In order to evaluate the efficiencies of the proposed binary algorithm, 22 benchmark functions are employed and divided into three groups: unimodal, multimodal, and composite. The experimental results confirm better performance of BPSOGSA compared with binary gravitational search algorithm (BGSA), binary particle swarm optimization (BPSO), and genetic algorithm in terms of avoiding local minima and convergence rate.
In order to overcome the poor exploitation of the krill herd (KH) algorithm, a hybrid differential evolution KH (DEKH) method has been developed for function optimization. The improvement involves adding a new hybrid differential evolution (HDE) operator into the krill, updating process for the purpose of dealing with optimization problems more efficiently. The introduced HDE operator inspires the intensification and lets the krill perform local search within the defined region. DEKH is validated by 26 functions. From the results, the proposed methods are able to find more accurate solution than the KH and other methods. In addition, the robustness of the DEKH algorithm and the influence of the initial population size on convergence and performance are investigated by a series of experiments.
This paper develops a methodology to obtain the optimum design of the gravity and reinforced cantilever retaining walls in terms of least-cost, having different cases of backfill satisfying the stability criteria, according to the height and properties of earth that the wall are required to support. An Enhanced Charged System Search Algorithm (ECSS) is utilized to find the economical sections as the output after minimizing the cost. The ECSS is one of the recently developed meta-heuristic algorithms that is inspired by the Coulomb and Gauss’s laws of electrostatics in physics. In order to evaluate the efficiency of this algorithm, some numerical examples are utilized. Comparing the results of the retaining wall designs obtained by the other methods illustrates a good performance of the ECSS.
Purpose
– To improve the performance of the krill herd (KH) algorithm, in this paper, a series of chaotic particle-swarm krill herd (CPKH) algorithms are proposed for solving optimization tasks within limited time requirements. The paper aims to discuss these issues.
Design/methodology/approach
– In CPKH, chaos sequence is introduced into the KH algorithm so as to further enhance its global search ability.
Findings
– This new method can accelerate the global convergence speed while preserving the strong robustness of the basic KH.
Originality/value
– Here, 32 different benchmarks and a gear train design problem are applied to tune the three main movements of the krill in CPKH method. It has been demonstrated that, in most cases, CPKH with an appropriate chaotic map performs superiorly to, or at least highly competitively with, the standard KH and other population-based optimization methods.
Recently, Gandomi and Alavi proposed a meta-heuristic optimization algorithm, called Krill Herd (KH). This paper introduces the chaos theory into the KH optimization process with the aim of accelerating its global convergence speed. Various chaotic maps are considered in the proposed chaotic KH (CKH) method to adjust the three main movements of the krill in the optimization process. Several test problems are utilized to evaluate the performance of CKH. The results show that the performance of CKH, with an appropriate chaotic map, is better than or comparable with the KH and other robust optimization approaches.
This study presents an improved krill herd (IKH) approach to solve global optimization problems. The main improvement pertains to the exchange of information between top krill during motion calculation process to generate better candidate solutions. Furthermore, the proposed IKH method uses a new Lévy flight distribution and elitism scheme to update the KH motion calculation. This novel meta-heuristic approach can accelerate the global convergence speed while preserving the robustness of the basic KH algorithm. Besides, the detailed implementation procedure for the IKH method is described. Several standard benchmark functions are used to verify the efficiency of IKH. Based on the results, the performance of IKH is superior to or highly competitive with the standard KH and other robust population-based optimization methods.
Krill herd (KH) is a novel search heuristic method. To improve its performance, a biogeography-based krill herd (BBKH) algorithm is presented for solving complex optimization tasks. The improvement involves introducing a new krill migration (KM) operator when the krill updating to deal with optimization problems more efficiently. The KM operator emphasizes the exploitation and lets the krill cluster around the best solutions at the later run phase of the search. The effects of these enhancements are tested by various well-defined benchmark functions. Based on the experimental results, this novel BBKH approach performs better than the basic KH and other optimization algorithms.
The Multi-Layer Perceptron (MLP), as one of the most-widely used Neural Networks (NNs), has been applied to many practical problems. The MLP requires training on specific applications, often experiencing problems of entrapment in local minima, convergence speed, and sensitivity to initialization. This paper proposes the use of the recently developed Biogeography-Based Optimization (BBO) algorithm for training MLPs to reduce these problems. In order to investigate the efficiencies of BBO in training MLPs, five classification datasets, as well as six function approximation datasets are employed. The results are compared to five well-known heuristic algorithms, Back Propagation (BP), and Extreme Learning Machine (ELM) in terms of entrapment in local minima, result accuracy, and convergence rate. The results show that training MLPs by using BBO is significantly better than the current heuristic learning algorithms and BP. Moreover, the results show that BBO is able to provide very competitive results in comparison with ELM.
A novel robust meta-heuristic optimization algorithm, which can be considered as an improvement of the recently developed firefly algorithm, is proposed to solve global numerical optimization problems. The improvement includes the addition of information exchange between the top fireflies, or the optimal solutions during the process of the light intensity updating. The detailed implementation procedure for this improved meta-heuristic method is also described. Standard benchmarking functions are applied to verify the effects of these improvements and it is illustrated that, in most situations, the performance of this improved firefly algorithm (IFA) is superior to or at least highly competitive with the standard firefly algorithm, a differential evolution method, a particle swarm optimizer, and a biogeography-based optimizer. Especially, this new method can accelerate the global convergence speed to the true global optimum while preserving the main feature of the basic FA.
The applicability of peer-to-peer (p2p) in the domain of grid computing has been an important subject over the past years. Nevertheless, the sole merger between p2p and the concept of grid is not sufficient to guarantee nontrivial efficiency. Some claim that ant colony optimization (ACO) algorithms might provide a definite answer to this question. However, the use of ACO in grid networks causes several problems. The first and foremost stems out of the fact that ACO algorithms usually perform well under the conditions of static networks, solving predetermined problems in a known and bound space. The question that remains to be answered is whether the evolutive component of these algorithms is able to cope with changing conditions; and by those we mean changes both in the positive sense, such as the appearance of new resources, but also in the negative sense, such as the disappearance or failure of fragments of the network. In this paper we study these considerations in depth, bearing in mind the specificity of the peer-to-peer nature.
Particle Swarm Optimization (PSO) is one of the most widely used heuristic algorithms. The simplicity and inexpensive computational cost makes this algorithm very popular and powerful in solving a wide range of problems. The binary version of this algorithm has been introduced for solving binary problems. The main part of the binary version is a transfer function which is responsible to map a continuous search space to a discrete search space. Currently there appears to be insufficient focus on the transfer function in the literature despite its apparent importance. In this study six new transfer functions divided into two families, s-shaped and v-shaped, are introduced and evaluated. Twenty-five benchmark optimization functions provided by CEC 2005 special session are employed to evaluate these transfer functions and select the best one in terms of avoiding local minima and convergence speed. In order to validate the performance of the best transfer function, a comparative study with six recent modifications of BPSO is provided as well. The results prove that the new introduced v-shaped family of transfer functions significantly improves the performance of the original binary PSO.
Due to shortcoming of traditional image matching for computing the fitness for every pixel in the searching space, a new bat algorithm with mutation (BAM) is proposed to solve image matching problem, and a modification is applied to mutate between bats during the process of the new solutions updating. This new approach can accelerate the global convergence speed while preserving the strong robustness of the basic BA. The realization procedure for this improved meta-heuristic approach BAM is also presented. To prove the performance of this proposed meta-heuristic method, BAM is compared with BA and other population-based optimization methods, DE and SGA. The experiment shows that the proposed approach is more effective and feasible in image matching than the other model.
An improved discrete immune optimization algorithm based on particle swarm optimization (IDIPSO) is proposed for Quality of Service (QoS)-driven web service composition with global QoS constraints. A series of effective strategies are presented for this problem, which include an improved local best first strategy based on mathematical analysis for candidate service selection, a perturbing global best strategy along the global best particle. The improved local best first strategy has equivalent effects on the local fitness of a candidate service and the fitness of a composite web service. Empirical comparisons with recently proposed algorithms on various scales of composite web service instances with global QoS constraints indicate that IDIPSO is highly competitive in terms of powerful searching capability, high stability and well trade-off between population diversity and selection pressure, especially when the size of the composite web service problem is large.
Recently, Gandomi and Alavi proposed a robust meta-heuristic optimization algorithm, called Krill Herd (KH), for global optimization. To improve the performance of the KH algorithm, harmony search (HS) is applied to mutate between krill during the process of krill updating instead of physical diffusion used in KH. A novel hybrid meta-heuristic optimization approach HS/KH is proposed to solve global numerical optimization problem. HS/KH combines the exploration of harmony search (HS) with the exploitation of KH effectively, and hence, it can generate the promising candidate solutions. The detailed implementation procedure for this improved meta-heuristic method is also described. Fourteen standard benchmark functions are applied to verify the effects of these improvements, and it is demonstrated that, in most cases, the performance of this hybrid meta-heuristic method (HS/KH) is superior to, or at least highly competitive with, the standard KH and other population-based optimization methods, such as ACO, BBO, DE, ES, GA, HS, KH, PSO, and SGA. The effect of the HS/FA parameters is also analyzed.