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In many disciplines, including pattern recognition, data mining, machine learning, image analysis, and bioinformatics, data clustering is a common analytical tool for data statistics. The majority of conventional clustering techniques are slow to converge and frequently get stuck in local optima. In this regard, population-based meta-heuristic algorithms are used to overcome the problem of getting trapped in local optima and increase the convergence speed. An asymmetric approach to clustering the asymmetric self-organizing map is proposed in this paper. The Interactive Autodidactic School (IAS) is one of these population-based metaheuristic and asymmetry algorithms used to solve the clustering problem. The chaotic IAS algorithm also increases exploitation and generates a better population. In the proposed model, ten different chaotic maps and the intra-cluster summation fitness function have been used to improve the results of the IAS. According to the simulation findings, the IAS based on the Chebyshev chaotic function outperformed other chaotic IAS iterations and other metaheuristic algorithms. The efficacy of the proposed model is finally highlighted by comparing its performance with optimization algorithms in terms of fitness function and convergence rate. This algorithm can be used in different engineering problems as well. Moreover, the Binary IAS (BIAS) detects coronavirus disease 2019 (COVID-19). The results demonstrate that the accuracy of BIAS for the COVID-19 dataset is 96.25%.

Clustering is a method of data analysis and one of the important methods in data mining that has been considered by researchers in many fields as well as in many disciplines. In this paper, we propose combining WOA with BA for data clustering. To assess the efficiency of the proposed method, it has been applied in data clustering. In the proposed method, first, by examining BA thoroughly, the weaknesses of this algorithm in exploitation and exploration are identified. The proposed method focuses on improving BA exploitation. Therefore, in the proposed method, instead of the random selection step, one solution is selected from the best solutions, and some of the dimensions of the position vector in BA are replaced We change some of the best solutions with the step of reducing the encircled mechanism and updating the WOA spiral, and finally, after selecting the best exploitation between the two stages of WOA exploitation and BA exploitation, the desired changes are applied on solutions. We evaluate the performance of the proposed method in comparison with other meta-heuristic algorithms in the data clustering discussion using six datasets. The results of these experiments show that the proposed method is statistically much better than the standard BA and also the proposed method is better than the WOA. Overall, the proposed method was more robust and better than the Harmony Search Algorithm (HAS), Artificial Bee Colony (ABC), WOA and BA.

Metaheuristic algorithms are used to solve NP-hard optimization problems. These algorithms have two main components, i.e. exploration and exploitation, and try to strike a balance between exploration and exploitation to achieve the best possible near-optimal solution. The bat algorithm is one of the metaheuristic algorithms with poor exploration and exploitation. In this paper, exploration and exploitation processes of Gray Wolf Optimizer (GWO) algorithm are applied to some of the solutions produced by the bat algorithm. Therefore, part of the population of the bat algorithm is changed by two processes (i.e. exploration and exploitation) of GWO; the new population enters the bat algorithm population when its result is better than that of the exploitation and exploration operators of the bat algorithm. Thereby, better new solutions are introduced into the bat algorithm at each step. In this paper, 20 mathematic benchmark functions are used to evaluate and compare the proposed method. The simulation results show that the proposed method outperforms the bat algorithm and other metaheuristic algorithms in most implementations and has a high performance.

A successful software should be finalized with determined and predetermined cost and time. Software is a production which its approximate cost is expert workforce and professionals. The most important and approximate software cost estimation (SCE) is related to the trained workforce. Creative nature of software projects and its abstract nature make extremely cost and time of projects difficult to estimate. Various methods have been presented in the software project cost estimation for performing a software project in the area of software engineering. COCOMO II model is one of the most documented models among template-based methods that has been proposed by Bohm. Common methods for estimating the time and cost are essentially abstract, accordingly, providing new methods for SCE is required and necessary. In this paper, a new method is presented to solve the problem of SCE by using hybrid particle swarm optimization (PSO) algorithm and K-nearest neighbor (KNN) algorithm. The method was evaluated on 6 multiple datasets with 8 different evaluation criteria. Obtained results show the more accurate performance of the proposed method.

Travelling Salesman Problem (TSP) is a discrete hybrid optimization problem considered NP-hard. TSP aims to discover the shortest Hamilton route that visits each city precisely once and then returns to the starting point, making it the shortest route feasible. This paper employed a Farmland Fertility Algorithm (FFA) inspired by agricultural land fertility and a hyper-heuristic technique based on the Modified Choice Function (MCF). The neighborhood search operator can use this strategy to automatically select the best heuristic method for making the best decision. Lin-Kernighan (LK) local search has been incorporated to increase the efficiency and performance of this suggested approach. 71 TSPLIB datasets have been compared with different algorithms to prove the proposed algorithm’s performance and efficiency. Simulation results indicated that the proposed algorithm outperforms comparable methods of average mean computation time, average percentage deviation (PDav), and tour length. [ABSTRACT FROM AUTHOR]

In many disciplines, including pattern recognition, data mining, machine learning, image analysis, and bioinformatics, data clustering is a common analytical tool for data statistics. The majority of conventional clustering techniques are slow to converge and frequently get stuck in local optima. In this regard, population-based meta-heuristic algorithms are used to overcome the problem of getting trapped in local optima and increase the convergence speed. An asymmetric approach to clustering the asymmetric self-organizing map is proposed in this paper. The Interactive Autodidactic School (IAS) is one of these population-based metaheuristic and asymmetry algorithms used to solve the clustering problem. The chaotic IAS algorithm also increases exploitation and generates a better population. In the proposed model, ten different chaotic maps and the intra-cluster summation fitness function have been used to improve the results of the IAS. According to the simulation findings, the IAS based on the Chebyshev chaotic function outperformed other chaotic IAS iterations and other metaheuristic algorithms. The efficacy of the proposed model is finally highlighted by comparing its performance with optimization algorithms in terms of fitness function and convergence rate. This algorithm can be used in different engineering problems as well. Moreover, the Binary IAS (BIAS) detects coronavirus disease 2019 (COVID-19). The results demonstrate that the accuracy of BIAS for the COVID-19 dataset is 96.25%. [ABSTRACT FROM AUTHOR]

Metaheuristics play a critical role in solving optimization problems, and most of them have been inspired by the collective intelligence of natural organisms in nature. This paper proposes a new metaheuristic algorithm inspired by gorilla troops' social intelligence in nature, called Artificial Gorilla Troops Optimizer (GTO). In this algorithm, gorillas' collective life is mathematically formulated, and new mechanisms are designed to perform exploration and exploitation. To evaluate the GTO, we apply it to 52 standard benchmark functions and seven engineering problems. Friedman's test and Wilcoxon rank‐sum statistical tests statistically compared the proposed method with several existing metaheuristics. The results demonstrate that the GTO performs better than comparative algorithms on most benchmark functions, particularly on high‐dimensional problems. The results demonstrate that the GTO can provide superior results compared with other metaheuristics. [ABSTRACT FROM AUTHOR]

Solving constrained engineering optimization problems is a highly significant issue, and many different approaches have been proposed in this regard. In this article, a modified farmland fertility algorithm (FFA) has been proposed. This algorithm improves new solutions by benefiting from neighborhoods produced by the new method. In the proposed algorithm, the FFA algorithm phases are modified in the update functions, and some of its variables are replaced with new ones. In the first phase of the algorithm, a mutation is also used to improve the solution of the problem with a particular rule. The results on CEC2019 standard functions were examined to determine the impact of the new parameters. Experiments were performed on 26 standard functions and two constrained engineering optimization problems. To make a better comparison, the analysis of variance, without parameters such as the Friedman test and Pairwise test, was used to evaluate and compare the algorithms. These experiments showed that the proposed algorithm is capable of high‐speed convergence and can minimize most standard functions and engineering constraints within the minimum time and the least function evaluation to the optimum value. The proposed FFA could outperform the previous version of the FFA with minimum modification in convergence, CPU‐time, and complexity. [ABSTRACT FROM AUTHOR]

Traveling Salesman Problem (TSP) is an intricate discrete hybrid optimization problem that is categorized as an NP‐Hard problem. The objective of the TSP is to find the shortest Hamilton route between cities to visit all existing cities and returning to the original city, from which the route started. Various researches have been carried out on TSP, and manifold solutions have been proposed to find the shortest route between cities, but none has been able to solve this problem completely. In this paper, a novel discrete version of the Farmland Fertility Algorithm is proposed, which uses three neighborhood searching mechanisms, and one Crossover operator. The Metropolis Acceptance Criterion has also been utilized to evade the local optimal traps. Furthermore, the Roulette Wheel selection technique is used to select neighboring mechanisms during the optimization process. Moreover, a local search mechanism is used to maximize the performance of the proposed algorithm. To prove the effectiveness of the contributions, and illustrate the efficiency of the proposed algorithm, the TSP library is evaluated on 37 data sets and compared with some well‐known similar methods. The simulation results showed the superiority of the proposed algorithm against other comparative methods. [ABSTRACT FROM AUTHOR]

Summary: There exist numerous high‐dimensional problems in the real world which cannot be solved through the common traditional methods. The metaheuristic algorithms have been developed as successful techniques for solving a variety of complex and difficult optimization problems. Notwithstanding their advantages, these algorithms may turn out to have weak points such as lower population diversity and lower convergence rate when facing complex high‐dimensional problems. An appropriate approach to solve such problems is to apply multi‐agent systems (MASs) along with the metaheuristic algorithms. The present paper proposes a new approach based on the MASs and the concept of agent, which is named MAS as Metaheuristic (MAMH) method. In the proposed method, several basic and powerful metaheuristic algorithms are considered as separate agents, each of which sought to achieve its own goals while competing and cooperating with others to achieve the common goals. Altogether, the proposed method was tested on 32 complex benchmark functions, the results of which indicated the effectiveness and powerfulness of the proposed method for solving high‐dimensional optimization problems. In addition, in this paper, the binary version of the proposed method, called Binary MAMH (BMAMH), was implemented on the email spam detection. According to the results, the proposed method exhibited a higher degree of precision in the detection of spam emails compared to other metaheuristic algorithms and methods. [ABSTRACT FROM AUTHOR]

Feature selection (FS) in data mining is one of the most challenging and most important activities in pattern recognition. In this article, a new hybrid model of whale optimization algorithm (WOA) and flower pollination algorithm (FPA) is presented for the problem of FS based on the concept of opposition‐based learning (OBL) which name is HWOAFPA. The procedure is that the WOA is run first and at the same time during the run, the WOA population is changed by the OBL. And, to increase the accuracy and speed of convergence, it is used as the initial population of FPA. To evaluate the performance of the proposed method, experiments were carried out in two steps. The experiments were performed on 10 datasets from the UCI data repository and Email spam detection datasets. The results obtained from the first step showed that the proposed method was more successful in terms of the average size of selection and classification accuracy than other basic metaheuristic algorithms. In addition, the results from the second step showed that the proposed method which was a run on the Email spam dataset performed much more accurately than other similar algorithms in terms of accuracy of Email spam detection. [ABSTRACT FROM AUTHOR]

One method to increase classifier accuracy is using Feature Selection (FS). The main idea in the FS is reducing complexity, eliminating irrelevant information, and deleting a subset of input features that either have little information or have no information for prediction. In this paper, three efficient binary methods based on the Symbiotic Organisms Search (SOS) algorithm were presented for solving the FS problem. In the first and second methods, several S_shaped and V_shaped transfer functions were used for the binarization of the SOS, respectively. These methods were called BSOSS and BSOSV. In the third method, two new operators called Binary Mutualism Phase (BMP) and Binary Commensalism Phase (BCP) were presented for binarization of the SOS, named Efficient Binary SOS (EBSOS). The proposed methods were run on 18 standard UCI datasets and compared to the base and important meta-heuristic algorithms. The test results showed that the EBSOS method has the best performance among the three proposed methods for the binarization of the SOS. Finally, the EBSOS method was compared to the Genetic Algorithm (GA), Binary Bat Algorithm (BBA), Binary Particle Swarm Optimization (BPSO) Algorithm, Binary Flower Pollination Algorithm (BFPA), Binary Grey Wolf Optimizer (BGWO) Algorithm, Binary Dragonfly Algorithm (BDA), and Binary Chaotic Crow Search Algorithm (BCCSA). In addition, the EBSOS method was executed on the spam email dataset with the KNN, NB, SVM, and MLP classifiers. The results showed that the EBSOS method has better performance compared to other methods in terms of feature count and accuracy criteria. Furthermore, it was practically evaluated on spam email detection in particular. [ABSTRACT FROM AUTHOR]

Nowadays, the existence of continuous optimization problems has led researchers to come up with a variety of methods to solve continues optimization problems. The metaheuristic algorithms are one of the most popular and common ways to solve continuous optimization problems. Firefly Algorithm (FA) is a successful metaheuristic algorithm for solving continuous optimization problems; however, although this algorithm performs very well in local search, it has weaknesses and disadvantages in finding solution in global search. This problem has caused this algorithm to be trapped locally and the balance between exploration and exploitation cannot be well maintained. In this paper, three different approaches based on the Dragonfly Algorithm (DA) processes and the OBL method are proposed to improve exploration, performance, efficiency and information-sharing of the FA and to avoid the FA getting stuck in local trap. In the first proposed method (FADA), the robust processes of DA are used to improve the exploration, performance and efficiency of the FA; and the second proposed method (OFA) uses an Opposition-Based Learning (OBL) algorithm to accelerate the convergence and exploration of the FA. Finally, in the third approach, which is referred to as OFADA in this paper, a hybridization of the hybrid FADA and the OBL method is used to improve the convergence and accuracy of the FA. The three proposed methods were implemented on functions with 2, 4, 10, and 30 dimensions. The results of the implementation of these three proposed methods showed that OFADA approach outperformed the other two proposed methods and other compared metaheuristic algorithms in different dimensions. In addition, all the three proposed methods provided better results compared with other metaheuristic algorithms on small-dimensional functions. However, performance of many metaheuristic algorithms decreased with increasing the dimensions of the functions. While the three proposed methods, in particular the OFADA approach, have been able to make better converge with the higher-dimensional optimization functions toward the target in comparison with other metaheuristic algorithms, and to show a high performance. [ABSTRACT FROM AUTHOR]

Whale Optimization Algorithm (WOA) is an optimization algorithm developed by Mirjalili and Lewis in 2016. An overview of WOA is described in this paper, rooted from the bubble-net hunting strategy, besides an overview of WOA applications that are used to solve optimization problems in various categories. The best solution has been determined to make something as functional and effective as possible through the optimization process by minimizing or maximizing the parameters involved in the problems. Research and engineering attention have been paid to Meta-heuristics for purposes of decision-making given the growing complexity of models and the needs for quick decision making in the engineering. An updated review of research of WOA is provided in this paper for hybridization, improved, and variants. The categories included in the reviews are Engineering, Clustering, Classification, Robot Path, Image Processing, Networks, Task Scheduling, and other engineering applications. According to the reviewed literature, WOA is mostly used in the engineering area to solve optimization problems. Providing an overview and summarizing the review of WOA applications are the aims of this paper. [ABSTRACT FROM AUTHOR]

Highlights • In this paper, a new metaheuristic algorithm that is inspired by farmland fertility in nature is presented which is evaluated by using large number of mathematical problem of standard benchmark. • We compared it with other powerful metaheuristic algorithms such as: ABC, FA, HS, PSO, DA, BA and improved PSO. • The proposed metaheuristic algorithm is very simple and very flexible than other available metaheuristic algorithms. And with smaller dimensions problems has been able to act as a strong metaheuristic algorithm and it has optimized problems nicely. Abstract Nowadays, the use of metaheuristic algorithms has dramatically increased in order to achieve the optimal solution in solving continuous optimization problems. In this paper, a new metaheuristic algorithm that is inspired by farmland fertility in nature is presented; this algorithm divides into several parts of the farmland, and to optimize solutions of each section with optimal efficiency of two types in internal and external memory. In order to evaluate the farmland fertility, we simulated it on 20 main function of mathematical optimization that is important to evaluate this type of algorithms and the results displayed. This farmland fertility has been compared with other metaheuristic algorithms such as; artificial bee colony (ABC), firefly algorithm (FA), harmony search (HS), particle swarm optimization (PSO), differential evolution (DE), bat algorithm (BA), and improved PSO and the results are displayed clearly. Simulations show that the farmland fertility often acts better than other metaheuristic algorithms. The farmland fertility in problems with smaller dimensions problems has been able to act as a strong metaheuristic algorithm and it has optimized problems nicely. Furthermore, the farmland fertility in problems with larger dimensions has been able to perform better than other algorithms. The effectiveness of other algorithms decreases significantly with number of dimensions and the farmland fertility obtains better results than other algorithms. [ABSTRACT FROM AUTHOR]

• A novel meta-heuristic is proposed inspired by the social life and hierarchy of wild mountain gazelles. • Gazelles' hierarchical and social life is formulated mathematically and used to develop an optimization algorithm. • The proposed algorithm is evaluated and tested using several test beds and case studies. • The results demonstrate that the proposed algorithm performs better than the comparable algorithms. The Mountain Gazelle Optimizer (MGO), a novel meta-heuristic algorithm inspired by the social life and hierarchy of wild mountain gazelles, is suggested in this paper. In this algorithm, gazelles' hierarchical and social life is formulated mathematically and used to develop an optimization algorithm. The MGO algorithm is evaluated and tested using Fifty-two standard benchmark functions and seven different engineering problems. It is compared with nine other powerful meta-heuristic algorithms to validate the result. The significant differences between the comparative algorithms are demonstrated using Wilcoxon's rank-sum and Friedman's tests. Numerous experiments have shown that the MGO performs better than the comparable algorithms on utmost benchmark functions. In addition, according to the tests performed, the MGO maintains its search capabilities and shows good performance even when increasing the dimensions of optimization problems. The source codes of the MGO algorithm are publicly available at https://www.mathworks.com/matlabcentral/fileexchange/118680-mountain-gazelle-optimizer. [ABSTRACT FROM AUTHOR]

To optimize the exopolysaccharide (EPS) production by Lactobacillus rhamnosus 519, fermentation experiments using response surface methodology (RSM) were carried out. Studying the effect of different carbon and nitrogen sources on EPS production in skimmed milk (SM) resulted in selecting of sucrose and yeast nitrogen base (YNB) for the optimization experiments. The effect of pH, incubation time, YNB concentration and sucrose concentration on EPS production in SM was studied. The results showed that pH, incubation time and sucrose concentration have significant effect on EPS production ( P ≤0.05). Maximum EPS production was 256 mg/L and the optimum pH, incubation time, YNB concentration and sucrose concentration for EPS production were found to be 5.7, 49 h, 9.8 g/L and 25.8 g/L, respectively. [ABSTRACT FROM AUTHOR]

• An improved version of SOS (HCLSOS) is presented to tackle optimization problems. • HCLSOS implements a multiswarm strategy enhanced with comprehensive learning, gaussian scaled commensalism, and a unidirectional information exchange. • The various mechanisms achieved the needed balance between exploration and exploitation. • Standard / CEC2014 / CEC2017 / CEC2022 benchmark functions and constrained engineering problems are tested. • HCLSOS is applied to design an efficient frequency control of a two-area islanded microgrid system • HCLSOS demonstrates superiority to its competing peers in many problems. The Symbiotic Organism Search (SOS) algorithm, and most of its variants, exhibit the same search behaviour. That is, each organism or individual in the ecosystem applies the same update rules according to 3 phases: mutualism, commensalism, and parasitism. By applying this strategy, an organism is capable of crippling the search effort of another organism. To overcome this problem, this paper proposes a Heterogeneous Comprehensive Learning SOS (HCLSOS) which divides the population into two distinct subpopulations namely: the exploration and exploitation subpopulations. HCLSOS allows each organism to follow one of two search behaviour according to into its subpopulation: whether to explore or exploit. This addresses the problem of balancing exploration and exploitation in SOS. The proposed algorithm employs a comprehensive learning strategy for the exploration group to generate a new type of mutual vector called the multispecies mutual vector capable of preserving organisms' diversity and discouraging premature convergence. Information exchange between the two groups is unidirectional and managed through a random elite learning strategy. Through this collaboration, HCLSOS can effectively evolve organisms to explore the search space and properly exploit the discovered optimal regions. The study tested HCLSOS on 23 benchmark functions, CEC2014, CEC2017, and the recent CEC 2022 test suites. The outcome of HCLSOS is compared with 15 state-of-the-art algorithms, and the results obtained showed that HCLSOS can attain competitive or even better results. Furthermore, we applied HCLSOS to solve 3 constrained engineering problems and to design an efficient frequency control of a two-area islanded microgrid system. [ABSTRACT FROM AUTHOR]