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Artificial rabbits optimization (ARO) is a recently proposed biology-based optimization algorithm inspired by the detour foraging and random hiding behavior of rabbits in nature. However, for solving optimization problems, the ARO algorithm shows slow convergence speed and can fall into local minima. To overcome these drawbacks, this paper proposes chaotic opposition-based learning ARO (COARO), an improved version of the ARO algorithm that incorporates opposition-based learning (OBL) and chaotic local search (CLS) techniques. By adding OBL to ARO, the convergence speed of the algorithm increases and it explores the search space better. Chaotic maps in CLS provide rapid convergence by scanning the search space efficiently, since their ergodicity and non-repetitive properties. The proposed COARO algorithm has been tested using thirty-three distinct benchmark functions. The outcomes have been compared with the most recent optimization algorithms. Additionally, the COARO algorithm's problem-solving capabilities have been evaluated using six different engineering design problems and compared with various other algorithms. This study also introduces a binary variant of the continuous COARO algorithm, named BCOARO. The performance of BCOARO was evaluated on the breast cancer dataset. The effectiveness of BCOARO has been compared with different feature selection algorithms. The proposed BCOARO outperforms alternative algorithms, according to the findings obtained for real applications in terms of accuracy performance, and fitness value. Extensive experiments show that the COARO and BCOARO algorithms achieve promising results compared to other metaheuristic algorithms. [ABSTRACT FROM AUTHOR]

This paper focuses on improving the overall performance of the vegetation evolution (VEGE) algorithm and proposes a chaotic VEGE with multiple seeding strategies and a mutation module (CVEGE). While the original VEGE exhibits robust exploitation capabilities, it falls short in terms of exploration and overcoming local optima. Thus, we introduce the chaotic local search operators, multiple seed dispersion strategies, and a unique mutation module to address these mentioned limitations. Furthermore, we incorporate a simplified sigmoid transfer function into CVEGE and propose a binary variant known as binary chaotic vegetation evolution (BCVEGE). In numerical experiments, we evaluate CVEGE on 10-D, 30-D, 50-D, and 100-D CEC2020 benchmark functions, as well as four engineering optimization problems. Additionally, BCVEGE is subjected to testing on two combinatorial optimization problems: wrapper-based feature selection tasks and classic 0/1 knapsack problems. Here, we employ two classic algorithms (i.e. differential evolution and particle swarm optimization) and seven state-of-the-art competitor algorithms including the original VEGE as the competitor algorithms. The sufficient numerical experiments and statistical analysis practically show that our proposal: CVEGE and BCVEGE, are competitive with compared algorithms. Furthermore, the demonstrated performance and scalability of CVEGE and BCVEGE suggest their potential utility across a wide range of optimization tasks. [ABSTRACT FROM AUTHOR]

The advent of multi-Microgrid (MG) energy systems necessitates the optimization of management strategies to curtail operational costs. This paper introduces an innovative MG energy management strategy that integrates Chaotic Local Search (CLS) with Particle Swarm Optimization (PSO) to fulfill this requirement. Our approach leverages PSO for extensive global exploration and subsequently employs CLS to refine local searches, thereby ensuring the attainment of optimal global outcomes. To further enhance performance, we have crafted a PSO algorithmic framework underpinned by chaotic local search principles, aimed at circumventing regions of local optima. The study presents a comprehensive MG energy system model that encompasses a photovoltaic generation unit, battery energy storage, and a micro gas turbine. The experimental data corroborates that our proposed algorithm secures optimal solutions within a range of 48.2–51.7, outperforming others in achieving these optimal resolutions. When juxtaposed with Scenario 1, there is a significant reduction in both operational and primary energy conversion costs by 24.22 % and 31.39 %, respectively. In comparison to Scenario 2, these figures are reduced by an additional 3.08 % and 6.05 %, respectively. The research findings underscore the strategy's exceptional performance in optimization tasks, as illustrated by the simulation outcomes. The methodology's application to a micro-energy network substantiates its practical relevance. Collectively, this research offers a holistic solution for the optimization of MG energy systems, effectively merging theoretical progress with tangible practical applications.

The Harris Hawk Optimization algorithm (HHO) is a recently proposed meta-heuristic algorithm that simulates biological population predation scheduling in the original hawk algorithm design. A Multi-strategy improved Harris Hawk Optimization algorithm (MHHO) is proposed to address the shortcomings of the Harris Hawk Optimization algorithm such as insufficient exploitation capability, decreasing population diversity, and easily falling into local optimality. Firstly, a chaotic local search strategy is introduced into the Harris Hawk to improve the exploitation ability of the algorithm. The advantages of chaotic mapping are exploited to find better individuals by performing local search around the current individual. Secondly, to enhance the population diversity, an elite alternative pooling strategy is proposed. In addition, the distribution estimation strategy is used to improve the convergence efficiency of the algorithm by sampling the dominant population information to better guide the direction of population evolution. Experimental tests on CEC2017 demonstrate that the improved algorithm achieves a balance between convergence speed and global search ability. Finally, the practicality of the improved algorithm is demonstrated by applying it to solve engineering constrained problems. [ABSTRACT FROM AUTHOR]

The use of metaheuristics is one of the most encouraging methodologies for taking care of real-life problems. Bald eagle search (BES) algorithm is the latest swarm-intelligence metaheuristic algorithm inspired by the intelligent hunting behavior of bald eagles. In recent research works, BES algorithm has performed reasonably well over a wide range of application areas such as chemical engineering, environmental science, physics and astronomy, structural modeling, global optimization, engineering design, energy efficiency, etc. However, it still lacks adequate searching efficiency and has a tendency to stuck in local optima which affects the final outcome. This paper introduces a modified BES (mBES) algorithm that removes the shortcomings of the original BES algorithm by incorporating three improvements; Opposition-based learning (OBL), Chaotic Local Search (CLS), and Transition & Pharsor operators. OBL is embedded in different phases of the standard BES viz. initial population, selecting, searching in space, and swooping phases to update the positions of individual solutions to strengthen exploration, CLS is used to enhance the position of the best agent which will lead to enhancing the positions of all individuals, and Transition & Pharsor operators help to provide sufficient exploration–exploitation trade-off. The efficiency of the mBES algorithm is initially evaluated with 29 CEC2017 and 10 CEC2020 global optimization benchmark functions. In addition, the practicality of the mBES is tested with a real-world feature selection problem and five engineering design problems. Results of the mBES algorithm are compared against a number of classical metaheuristic algorithms using statistical metrics, convergence analysis, box plots, and the Wilcoxon rank sum test. In the case of composite CEC2017 test functions F21-F30, mBES wins against compared algorithms in 70% test cases, whereas for the rest of the test functions, it generates good results in 65% cases. The proposed mBES produces best performance in 55% of the CEC2020 test functions, whereas for the rest of the 45% test cases, it generated competitive results. On the other hand, for five engineering design problems, the mBES is the best among all compared algorithms. In the case of the feature selection problem, the mBES also showed competitiveness with the compared algorithms. Results and observations for all tested optimization problems show the superiority and robustness of the proposed mBES over the baseline metaheuristics. It can be safely concluded that the improvements suggested in the mBES are proved to be effective making it competitive enough to solve a variety of optimization problems.

A new chaotic search group algorithm (CSGA) is proposed in this study for simultaneous network reconfiguration and allocation of distributed generation (SNR-DG) problem with the objective of minimum real power loss in the radial distribution network (RDN). The CSGA is an improved metaheuristic algorithm, in which a chaotic local search strategy is incorporated with the original SGA, to enhance its search performance. The proposed CSGA was studied on 33-, 69-, 84- and 118-bus RDNs with three load levels. After SNR-DG application, the voltage profile and real power loss of the system had enhanced significantly. The outcomes yielded by CSGA were compared with those obtained from the original SGA and other techniques depicted in the literature. The comparative findings revealed that CSGA yielded better solution quality than SGA and other techniques. Hence, CSGA is the best technique to address the SNR-DG problem in RDNs.

The Aquila optimizer (AO) is a recently developed swarm algorithm that simulates the hunting behavior of Aquila birds. In complex optimization problems, an AO may have slow convergence or fall in sub-optimal regions, especially in high complex ones. This paper tries to overcome these problems by using three different strategies: restart strategy, opposition-based learning and chaotic local search. The developed algorithm named as mAO was tested using 29 CEC 2017 functions and five different engineering constrained problems. The results prove the superiority and efficiency of mAO in solving many optimization issues.

The use of metaheuristics is one of the most encouraging methodologies for taking care of real-life problems. Bald eagle search (BES) algorithm is the latest swarm-intelligence metaheuristic algorithm inspired by the intelligent hunting behavior of bald eagles. In recent research works, BES algorithm has performed reasonably well over a wide range of application areas such as chemical engineering, environmental science, physics and astronomy, structural modeling, global optimization, engineering design, energy efficiency, etc. However, it still lacks adequate searching efficiency and has a tendency to stuck in local optima which affects the final outcome. This paper introduces a modified BES (mBES) algorithm that removes the shortcomings of the original BES algorithm by incorporating three improvements; Opposition-based learning (OBL), Chaotic Local Search (CLS), and Transition & Pharsor operators. OBL is embedded in different phases of the standard BES viz. initial population, selecting, searching in space, and swooping phases to update the positions of individual solutions to strengthen exploration, CLS is used to enhance the position of the best agent which will lead to enhancing the positions of all individuals, and Transition & Pharsor operators help to provide sufficient exploration–exploitation trade-off. The efficiency of the mBES algorithm is initially evaluated with 29 CEC2017 and 10 CEC2020 global optimization benchmark functions. In addition, the practicality of the mBES is tested with a real-world feature selection problem and five engineering design problems. Results of the mBES algorithm are compared against a number of classical metaheuristic algorithms using statistical metrics, convergence analysis, box plots, and the Wilcoxon rank sum test. In the case of composite CEC2017 test functions F21-F30, mBES wins against compared algorithms in 70% test cases, whereas for the rest of the test functions, it generates good results in 65% cases. The proposed mBES produces best performance in 55% of the CEC2020 test functions, whereas for the rest of the 45% test cases, it generated competitive results. On the other hand, for five engineering design problems, the mBES is the best among all compared algorithms. In the case of the feature selection problem, the mBES also showed competitiveness with the compared algorithms. Results and observations for all tested optimization problems show the superiority and robustness of the proposed mBES over the baseline metaheuristics. It can be safely concluded that the improvements suggested in the mBES are proved to be effective making it competitive enough to solve a variety of optimization problems. [ABSTRACT FROM AUTHOR]

This study presents an enhanced model based on a new swarm intelligence algorithm called jellyfish search optimizer (JSO). The suggested model is called chaotic jellyfish search with enhanced swap operator (CJSESOS). The CJSESOS algorithm has two modifications to the original JSO algorithm, the first is the chaotic sequence generated by iterating a logistic map which is named CJSO. This enhancement discovers a creative solution by directing particles to different location of the search space, the second is enhanced swap sequence operator which increased the CJSO algorithm's ability to escape from local minimum by diversifying the results. The performance of the CJSESOS is evaluated using fourteen benchmark functions. The proposed model is applied to solve a discreate real life problem, named team formation (TF) problem which considered one of the most significant problems in computer science and optimization. TF problem is defined as creating the most effective team of experts in social network to carry out a task with the lowest possible cost. The proposed CJSESOS algorithm was tested for solving the TF problem with varying number of skills in different datasets. In addition, the proposed algorithm is compared to well-known optimization algorithms such as jellyfish search optimizer (JSO), particle swarm optimization (PSO), genetic algorithm (GA), grey wolf optimization (GWO), heap based optimizer (HBO), aquila optimizer (AO) and pelican optimization algorithm (POA). The simulation results show that the proposed model outperformed all the compared algorithms on the term of efficiency and accuracy. [ABSTRACT FROM AUTHOR]

Abstract Wind driven optimization (WDO) is a meta-heuristic algorithm based on swarm intelligence. The original selection method makes it easy to converge prematurely and trap in local optima. Maintaining population diversity can solve this problem well. Therefore, we introduce a new fitness-distance balance-based selection strategy to replace the original selection method, and add chaotic local search with selecting chaotic map based on memory to further improve the search performance of the algorithm. A chaotic wind driven optimization with fitness-distance balance strategy is proposed, called CFDBWDO. In the experimental section, we find the optimal parameter settings for the proposed algorithm. To verify the effect of the algorithm, we conduct comparative experiments on the CEC 2017 benchmark functions. The experimental results denote that the proposed algorithm has superior performance. Compared with WDO, CFDBWDO can gradually converge in function optimization. We further verify the practicality of the proposed algorithm with six real-world optimization problems, and the obtained results are all better than other algorithms.

Team Formation (TF) is considered one of the most significant problems in computer science and optimization. TF is defined as forming the best team of experts in a social network to complete a task with least cost. Many real-world problems, such as task assignment, vehicle routing, nurse scheduling, resource allocation, and airline crew scheduling, are based on the TF problem. TF has been shown to be a Nondeterministic Polynomial time (NP) problem, and high-dimensional problem with several local optima that can be solved using efficient approximation algorithms. This paper proposes two improved swarm-based algorithms for solving team formation problem. The first algorithm, entitled Hybrid Heap-Based Optimizer with Simulated Annealing Algorithm (HBOSA), uses a single crossover operator to improve the performance of a standard heap-based optimizer (HBO) algorithm. It also employs the simulated annealing (SA) approach to improve model convergence and avoid local minima trapping. The second algorithm is the Chaotic Heap-based Optimizer Algorithm (CHBO). CHBO aids in the discovery of new solutions in the search space by directing particles to different regions of the search space. During HBO's optimization process, a logistic chaotic map is used. The performance of the two proposed algorithms (HBOSA) and (CHBO) is evaluated using thirteen benchmark functions and tested in solving the TF problem with varying number of experts and skills. Furthermore, the proposed algorithms were compared to well-known optimization algorithms such as the Heap-Based Optimizer (HBO), Developed Simulated Annealing (DSA), Particle SwarmOptimization (PSO), GreyWolfOptimization (GWO), and Genetic Algorithm (GA). Finally, the proposed algorithms were applied to a real-world benchmark dataset known as the Internet Movie Database (IMDB). The simulation results revealed that the proposed algorithms outperformed the compared algorithms in terms of efficiency and performance, with fast convergence to the global minimum. [ABSTRACT FROM AUTHOR]

In the classification problem, datasets often have a large number of features, but not all features are useful for classification. A lot of irrelevant features may even reduce the performance. Feature selection is to remove irrelevant features by minimizing the number of the feature subset and minimizing the classification error rate.So it can be regarded as a multi-objective optimization problem. Because of its simple structure and easy implementation, Harris Hawks Optimization algorithm (HHO) is widely employed in optimization problems. In this paper, the multi-objective HHO is applied to address the feature selection problem. In order to improve the search ability of the algorithm, associative learning, grey wolf optimization and chaotic local search are introduced into it. An external repository is used to save non-dominant solution set. The results of feature selection on the sixteen University of California Irvine (UCI) datasets show that the proposed method can effectively remove redundant features and improve the classification performance of the algorithm.

A new chaotic search group algorithm (CSGA) is proposed in this study for simultaneous network reconfiguration and allocation of distributed generation (SNR-DG) problem with the objective of minimum real power loss in the radial distribution network (RDN). The CSGA is an improved metaheuristic algorithm, in which a chaotic local search strategy is incorporated with the original SGA, to enhance its search performance. The proposed CSGA was studied on 33-, 69-, 84- and 118-bus RDNs with three load levels. After SNR-DG application, the voltage profile and real power loss of the system had enhanced significantly. The outcomes yielded by CSGA were compared with those obtained from the original SGA and other techniques depicted in the literature. The comparative findings revealed that CSGA yielded better solution quality than SGA and other techniques. Hence, CSGA is the best technique to address the SNR-DG problem in RDNs. [ABSTRACT FROM AUTHOR]

This article proffers a novel quasi-oppositional chaotic Harris hawk's optimization (HHO) (QOCHHO) algorithm for interpreting global optimization problems. In the proposed QOCHHO algorithm, quasi-opposition based learning (QOBL) and chaotic local search (CLS) approaches are integrated with the basic HHO for better quality of solution and faster convergence. The idea of QOBL assists to explore new regions of the search space and offers superior exploration. Again, CLS guides the search process nearby the most favorable regions of the search space yielding superior exploitation. Thus, a superior balance between the exploration and the exploitation holds in the case of QOCHHO making this newly projected algorithm more robust as correlated to the HHO algorithm. To demonstrate and validate effectiveness of the suggested QOCHHO algorithm, twenty-nine benchmark test functions of various categories, varied complexities (i.e., unimodal, multimodal, fixed dimension and composite functions) and different dimensions (i.e., 30 and 100) are used for simulation experiments. The simulation results attained by the projected QOCHHO algorithm are compared with the results obtained by recently surfaced HHO and other state-of-the-art algorithms (i.e., particle swarm optimization, moth-flame optimization algorithm, grey wolf optimizer, sine cosine algorithm, salp swarm algorithm, whale optimization algorithm and multi verse optimization algorithm). The outcomes of the benchmark test functions evidence that the anticipated QOCHHO algorithm is able to offers better outcomes in terms of improved exploration, local optima circumvention and faster convergence characteristics. The proposed QOCHHO algorithm is further employed to decipher real world engineering optimization problem (i.e., optimal siting and sizing of distributed generation (DG) in IEEE 33-bus and practical Brazil 136-bus radial distribution system (RDS) considering different types of load models at three load levels) and proffers a real application of the suggested algorithm in the field of electrical engineering. The simulation outcomes evidence that the obtained location and size of DGs in the RDS may be feasible one and the suggested QOCHHO algorithm may be a promising optimization algorithm for the chosen engineering optimization application. [ABSTRACT FROM AUTHOR]

Favourable performance of designed controllers for Permanent magnet synchronous motors (PMSMs), deeply depends on accurate model and parameters of PMSM. This paper proposes two improved versions of particle swarm optimization (PSO) for identification of all-six electrical and mechanical parameters of PMSM. This research inserts two different strategies to overcome premature convergence of PSO. In the first version, the PSO is incorporated with quasi-opposition-based learning (QOBL) to be accelerated and also to diversify its search moves. In the second version of proposed improved PSO, in an attempt to diversify and manifold search moves, a chaotic local search is inserted in the PSO to further enhance its global search ability. Aforementioned algorithms are tested on problem of PMSM parameter identification and also 30 CEC2014 benchmark functions. The obtained results demonstrate that the proposed algorithms in this research beside of good solution quality are very effective and robust so that they produce similar and promising results over repeated runs. Subsequently, a comparison of the proposed algorithm with two recent well performing algorithms, i.e. covariance matrix adaptation-evolution strategy (CMA-ES) and success-history based adaptive differential evolution with linear population size reduction (L-SHADE) confirmed a comparable performance of our proposed algorithm. Statistical analysis of obtained results on CEC2014 functions by Wilcoxon test also indicated that the proposed algorithm has a significant performance over other compared state-of-art algorithms. [ABSTRACT FROM AUTHOR]

Optimization algorithms are widely used to solve large and complex optimization problems. In this paper, a human intelligence-based optimization technique, an adaptive chaotic class topper optimization (AC-CTO), is proposed to solve well-known optimization problems related to the power system, i.e., economic load dispatch and combined emission economic dispatch problem. In the proposed AC-CTO scheme, the performance of the classical class topper optimization (CTO) is improved. AC-CTO includes chaotic local search (CLS), adaptive improvement factor (AIF) and adaptive acceleration coefficient (ACC) so that searching and local minima avoidance ability of the proposed algorithm is improved. To validate the exploration, exploitation and local optimal avoidance capabilities of the proposed algorithm, twenty-nine benchmark functions are used. Further, AC-CTO is used to solve five test cases of an ELD problem. To show the effectiveness of AC-CTO, results obtained are compared with the existing results obtained using other well-known techniques. [ABSTRACT FROM AUTHOR]

As a meta-heuristic algorithm that simulates the intelligence of gray wolves, grey wolf optimizer (GWO) has a wide range of applications in practical problems. As a kind of local search, chaotic local search (CLS) has a strong ability to get rid of the local optimum due to its integration of chaotic maps. To enhance GWO, CLS is always incorporated into GWO to increase its population diversity and accelerate algorithm’s convergence. However, it is still unclear that how may chaotic maps should be used in CLS and how to embed them into GWO. To address these challenging issues, this paper studies both single and multiple chaotic maps incorporated GWOs. Extensive comparative experiments are conducted based on IEEE Congress on Evolutionary Computation (CEC) benchmark test suit. The results show that CLS incorporated GWOs generally perform better than the original GWO, suggesting the effectiveness of such hybridization. Moreover, a remarkable finding of this work is that the piecewise linear chaotic map (PWLCM) and Gaussian map have the most potential to improve the search performance of GWO. Additionally, CLS incorporated GWOs also perform significantly better than some other state-of-the-art meta-heuristic algorithms. This study not only gives more insights into the mechanism of how CLS makes influence on GWO, but also finds that the most suitable choice of chaotic map for it.

Inspired by the behaviors of animals in the state of starvation, hunger games search algorithm (HGS) is proposed. HGS has shown competitive performance among other meta-heuristic (MH) algorithms. However, HGS tends to stagnate in local optimal for some complex optimization problems and remains premature convergence. Therefore, to solve these problems and enhance the diversity of the population, a modified HGS based on the operators of the differential evolution algorithm (DE), chaotic local search (CLS) strategies, and evolutionary population dynamics technique (EPD) is proposed (named DECEHGS). The proposed DECEHGS algorithm consists of two stages: in the first stage, based on the animals’ behaviors, we use different evolutionary methods to update animals’ positions; in the second stage, the CLS strategy and EPD technique are combined to prevent premature convergence and stagnation in a local optimum. The proposed algorithm was evaluated using IEEE CEC2014 and IEEE CEC2017 mathematical functions and four engineering problems. The experimental results demonstrate that DECEHGS has competitive performance in global optimization tasks and engineering problems compared with state-of-the-art algorithms.

In this paper, Belleville Washer Search optimization algorithm, Papilionoidea Optimization Algorithm, Opposition-based Learning integrated with Papilionoidea Optimization Algorithm, Chaotic Local Search integrated with Papilionoidea Optimization Algorithm and then both the Opposition-based Learning and Chaotic Local Search combined with Papilionoidea Optimization Algorithm are designed to solve the optimization problem. Key objectives of the paper are the minimization of voltage deviation, voltage stability enhancement and power loss reduction. Belleville washer is coned-disc spring, which can encumber the length of its axis by static and dynamic mode. Belleville washers are helpful for modification since dissimilar breadth can be exchanged within and away and it can be used to attain vast tune of coil rate. They are perfect in circumstances wherever a weighty coil power is necessary with least complimentary distance end to end and firmness previous to attainment of firm elevation. Fundamental law of force has been employed to design the Belleville Washer Search optimization algorithm. Belleville washer impresses a force on the particle in horizontal direction and an external force pressure the particle in anti-clockwise direction. Belleville washer's force is equal to the peripheral force in the period of time the particle will be in balanced condition. Initially, the particle moves certain distance, and Belleville washer's force will tender a resistance over the particle during the time peripheral force acting over the particle. Stiffness property of the Belleville washer is tailored relative to the objective function. Force and movement of the particle have been observed. Papilionoidea Optimization Algorithm is based on performance of the Papilionoidea in nutrition foraging. Opposition-based Learning is a technique integrated with Papilionoidea Optimization Algorithm. Through this, exploration capability of the process will be enriched and convergence rate will be augmented. Then, in this work, Chaotic Local Search integrated with Papilionoidea Optimization Algorithm. Through this integration, the algorithm will balance the exploration and exploitation equally. Then, both the Opposition-based Learning and Chaotic Local Search has been integrated with Papilionoidea Optimization Algorithm. With and without voltage stability, index Belleville Washer Search optimization algorithm, Papilionoidea Optimization Algorithm, Opposition-based Learning integrated with Papilionoidea Optimization Algorithm, Chaotic Local Search integrated with Papilionoidea Optimization Algorithm and then both the Opposition-based Learning and Chaotic Local Search combined with Papilionoidea Optimization Algorithm are corroborated in standard IEEE 30-bus test systems. Proposed algorithms condensed the power loss competently with augmentation in voltage stability and minimization of voltage deviation. [ABSTRACT FROM AUTHOR]

Harris Hawks Optimization is a recently proposed algorithm inspired by the cooperative manner and chasing behavior of harris. However, from the experimental results, it can be noticed that HHO may fall in local optima or have a slow convergence curve in some complex optimization tasks. In this paper, an improved version of HHO called IHHO is proposed which enhances the performance of HHO by combining HHO with opposition-based learning (OBL), Chaotic Local Search (CLS), and a self-adaptive technique. In order to show the performance of the proposed algorithm, several experiments are conducted using the Standard IEEE CEC 2017 benchmark. IHHO is compared with the classical HHO and other 10 state-of-art algorithms. Moreover, IHHO is used to solve 5 constrained engineering problems. IHHO has also been applied to solve feature selection problem using 7 UCI dataset. The numerical results and analysis show the superiority of IHHO in solving real-world problems. [ABSTRACT FROM AUTHOR]

Even with the advantages of the sine cosine algorithm (SCA) in solving multimodal problems, there are some shortcomings for this method. We observe that the random patterns utilized in SCA cause an increasing attraction toward local optima. This study developed a rationalized version of this technique to deal with several representative benchmark cases with different dimensions. The improved algorithm combines the chaotic local search mechanism and Lévy flight operator with the core trends of SCA. The new variant is named as CLSCA. The Lévy flight with long jumps is adopted to boost the exploratory tendencies of the algorithm, while the chaotic local search mechanism is used as a local search for the destination point, which helps to further enhance the exploitation capability of SCA. Therefore, a suitable equilibrium between the exploration and exploitation can be kept in the CLSCA by two embedded patterns. To investigate the effectiveness and strength of the developed method, the CLSCA was tested on many benchmark functions, including different types of tasks such as single modal, multi-modal, hybrid, and composition functions. We compare the CLSCA with well-known optimizers, like particle swarm optimization (PSO) algorithm, grey wolf optimizer (GWO), SCA with differential evolution (SCADE), opposition-based SCA (OBSCA), fuzzy self-tuning PSO (FST_PSO), chaotic salp swarm algorithm (CSSA), and Chaotic whale optimizer (CWOA). Numerical experimental results demonstrate that the exploratory and exploitative properties of the classical SCA are clearly improved. The experimental results also show that our improved CLSCA is a better technique for different kinds of optimization tasks.

Spherical evolution is a recently proposed nature-inspired meta-heuristic algorithm which is proven to have nontrivial efficiency and effectiveness in solving complex optimization problems. However, it has some limitations caused by its inherent scale factor and dimension factor. Hypercube search and chaotic local search are two kinds of effective search mechanisms. To construct an algorithm which has better exploration and exploitation abilities, we propose a novel algorithm which contains triple distinct search dynamics (TDSD), i.e., spherical search, hypercube search and chaotic local search. Effective control among them enhances search performance of TDSD. It is verified on thirty CEC2017 benchmark functions and three real-world optimization problems.

The salp swarm algorithm (SSA) is a bio-heuristic optimization algorithm proposed in 2017. It has been proved that SSA has competitive results compared to several other well-known meta-heuristic algorithms on various optimization problem. However, like most meta-heuristic algorithms, SSA is prone to problems such as local optimal solution and a slow convergence rate. To solve these problems, a chaotic salp swarm algorithm based on opposition-based learning (OCSSA) is proposed. The application of opposition-based learning (OBL) guarantees a better convergence speed and better develops the search space. The chaotic local search (CLS) method is also introduced, which can improve the performance of the algorithm to obtain the global optimal solution. The performance of OCSSA is compared with that of the original SSA and some other meta-heuristic algorithms on 28 benchmark functions with unimodal or multimodal characteristics. The experimental results show that the performance of OCSSA, with an appropriate chaotic map, is better than or comparable with the SSA and other meta-heuristic algorithms.

The advent of multi-Microgrid (MG) energy systems necessitates the optimization of management strategies to curtail operational costs. This paper introduces an innovative MG energy management strategy that integrates Chaotic Local Search (CLS) with Particle Swarm Optimization (PSO) to fulfill this requirement. Our approach leverages PSO for extensive global exploration and subsequently employs CLS to refine local searches, thereby ensuring the attainment of optimal global outcomes. To further enhance performance, we have crafted a PSO algorithmic framework underpinned by chaotic local search principles, aimed at circumventing regions of local optima. The study presents a comprehensive MG energy system model that encompasses a photovoltaic generation unit, battery energy storage, and a micro gas turbine. The experimental data corroborates that our proposed algorithm secures optimal solutions within a range of 48.2-51.7, outperforming others in achieving these optimal resolutions. When juxtaposed with Scenario 1, there is a significant reduction in both operational and primary energy conversion costs by 24.22 % and 31.39 %, respectively. In comparison to Scenario 2, these figures are reduced by an additional 3.08 % and 6.05 %, respectively. The research findings underscore the strategy's exceptional performance in optimization tasks, as illustrated by the simulation outcomes. The methodology's application to a micro-energy network substantiates its practical relevance. Collectively, this research offers a holistic solution for the optimization of MG energy systems, effectively merging theoretical progress with tangible practical applications., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Ping Li reports a relationship with 10.13039/501100004262Zhengzhou University of Light Industry that includes: employment, non-financial support, and travel reimbursement. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Nature-inspired metaheuristic algorithms are often based on the first-order difference hypercube search style to search for optimum solutions. In contrast, the spherical evolution algorithm (SE) employs a spherical search style. SE is very effective; however, there is still room for improvement. In this study, we added a chaotic local search (CLS) to the SE to improve its performance. This CLS uses information from several chaotic maps and records each instance of success. The recorded historical success information guides the CLS to choose the chaotic map for the next iteration. In our experiment, we compare the chaotic spherical evolution algorithm (CSE) with the original SE and other metaheuristic algorithms. The test set consists of 29 benchmark functions from the CEC2017 benchmark set and 22 real-world optimization problems from the CEC2011 set. Additionally, the new parameter introduced in the CSE has also been briefly discussed. Experimental results indicate that the proposed CSE significantly performs better than its competitors. [ABSTRACT FROM AUTHOR]

The recently developed Bacterial Foraging Optimization algorithm (BFO) is a nature-inspired optimization algorithm based on the foraging behavior of Escherichia coli. Due to its simplicity and effectiveness, BFO has been applied widely in many engineering and scientific fields. However, when dealing with more complex optimization problems, especially high dimensional and multimodal problems, BFO performs poorly in convergence compared to other nature-inspired optimization techniques. In this paper, we therefore propose an improved BFO, termed ChaoticBFO, which combines two chaotic strategies to achieve a more suitable balance between exploitation and exploration. Specifically, a chaotic initialization strategy is incorporated into BFO for bacterial population initialization to achieve acceleration throughout early steps of the proposed algorithm. Then, a chaotic local search with a `shrinking' strategy is introduced into the chemotaxis step to escape from local optimum. The performance of ChaoticBFO was validated on 23 numerical well-known benchmark functions by comparing with 10 other competitive metaheuristic algorithms. Moreover, it was applied to two real-world benchmarks from IEEE CEC 2011. The experimental results demonstrate that ChaoticBFO is superior to its counterparts in both convergence speed and solution quality in most of the cases. This paper is of great significance for promoting the research, improvement and application of the BFO algorithm.

Brain-storm optimization (BSO), which is a population-based optimization algorithm, exhibits a poor search performance, premature convergence, and a high probability of falling into local optima. To address these problems, we developed the adaptive mechanism-based BSO (ABSO) algorithm based on the chaotic local search in this study. The adjustment of the search space using the local search method based on an adaptive self-scaling mechanism balances the global search and local development performance of the ABSO algorithm, effectively preventing the algorithm from falling into local optima and improving its convergence accuracy. To verify the stability and effectiveness of the proposed ABSO algorithm, the performance was tested using 29 benchmark test functions, and the mean and standard deviation were compared with those of five other optimization algorithms. The results showed that ABSO outperforms the other algorithms in terms of stability and convergence accuracy. In addition, the performance of ABSO was further verified through a nonparametric statistical test.

Real-world optimisation problems that are not endowed in mathematical characteristics like differentiability, convexity etc. require non-traditional optimisation approaches that explore the promising regions of the search space stochastically to achieve the optima of the problem. Grey Wolf Optimizer (GWO) is one of the efficient and recently developed approaches in the area of Swarm Intelligence to solve real-world optimisation problems over continuous space. However, in some cases, due to the insufficient diversity, GWO still suffers from the problem of stagnation in local optimums. Therefore, this article presents the novel algorithm OCS-GWO that enhances the performance of original GWO by introducing the opposition-based learning to approximate the closer search candidate solution to the global optima and chaotic local search for the exploitation of the search regions efficiently. In OCS-GWO, a chaotic local search is used for balancing the exploration and exploitation operators that are the underlying features of any stochastic search algorithm. The performance of the proposed algorithm OCS-GWO has been evaluated on a set of 23 standard benchmark test problems and on three engineering application problems – gear train, cantilever beam and speed reducer design problems. The experimental results on test problems and engineering applications confirm the efficiency and reliability of the proposed algorithm over original GWO. [ABSTRACT FROM AUTHOR]

• A multi-strategy boosted mutative WOA is proposed for global optimization. • A chaotic initialization strategy, a chaotic local search and Gaussian mutation are incorporated into WOA. • The extensive results on benchmark problems and three classical engineering design problems reveal the excellent performance. Image, graphical abstract This paper presents an improved Whale Optimization Algorithm (WOA) for global optimization. WOA is a recently introduced meta-heuristic algorithm mimicking the hunting behavior of humpback whales. Owing to its simplicity in exploratory and exploitative operators and the satisfactory efficacy, this algorithm has found its place among the well-established population-based approach utilized in many engineering and science areas. However, this method is easy to fall into local optimum when dealing with some optimization cases. In order to further enhance its exploratory and exploitative performance, three strategies are incorporated into the original method to keep a better balance between exploitation and exploration tendencies. First, the chaotic initialization phase is introduced into the optimizer to initiate the swarm of chaos-triggered whales. Then, Gaussian mutation is employed to intensify the diversity level of the evolving population. At last, a chaotic local search with a 'shrinking' strategy is used to enhance the exploitative leanings of the basic optimizer. In order to verify the effectiveness of the improved WOA, it is compared to four meta-heuristic and state-of-the-art evolutionary algorithms on representative benchmark functions. Trial results and simulations reveal that not only the proposed improved WOA is significantly better than those basic algorithms including original WOA but also it is superior to compared state-of-the-art approaches. Moreover, the proposed algorithm is successfully applied to realize three constrained engineering test cases, which the results suggest that the improved WOA can effectively deal with the constrained functions as well. [ABSTRACT FROM AUTHOR]

• This paper proposes a boosted MFO for global search and kernel extreme learning machines. • Two strategies have been introduced into MFO for a more stable balance. • The extensive results on benchmark problems and real datasets have been performed. • A hybrid kernel extreme learning machine model is established for financial stress prediction. Image, graphical abstract Moth-flame optimization algorithm (MFO) is a new nature-inspired meta-heuristic based on the navigation routine of moths in the environment known as transverse orientation. For some complex optimization tasks, especially high dimensional and multimodal problems, the conventional MFO may face problems in the convergence trends or be trapped into the local and deceptive optima. Therefore, in this study, two strategies have been introduced into the conventional MFO to get a more stable sense of balance between the exploration and exploitation propensities. First, Gaussian mutation is employed to increase the population diversity of MFO. Then, a chaotic local search is applied to the flame updating process of MFO for better exploiting the locality of the solutions. The proposed CLSGMFO approach was compared against a wide range of well-known classical metaheuristic algorithms (MAs) and various advanced MAs using 23 classical benchmark functions. It was shown that the designed CLSGMFO can outperform most of the popular MAs in terms of solution quality and convergence speed. Moreover, based on CLSGMFO, a hybrid kernel extreme learning machine model, which is called CLSGMFO-KELM, is established to deal with financial stress prediction scenarios. To investigate the effectiveness of the CLSGMFO-KELM model, the proposed hybrid system was tested on two widely used financial datasets and compared against a broad array of popular classifiers. The results demonstrate that the proposed learning scheme can offer a superior kernel extreme learning machine model with excellent predictive performance. Accordingly, the proposed CLSGMFO can serve as an effective and efficient computer-aided tool for financial prediction. [ABSTRACT FROM AUTHOR]

Determining the global optima of integer and mixed-integer nonlinear problems is a useful contribution in various engineering applications. Swarm intelligence is a well-known branch of nature-inspired algorithms which tries to determine the solution with the help of intelligent and collective behaviour of social creatures. Grey wolf optimizer (GWO) is one of the recently developed efficient algorithms which are quite popular nowadays. In the present study, first, the GWO is proposed for solving integer and mixed-integer optimization problems, and secondly, an improved version of GWO named IMI-GWO is proposed. The IMI-GWO attempts to alleviate from the major issues of premature convergence and slow convergence of classical GWO. In IMI-GWO, the opposition-based learning maintains the diversity and the chaotic search locally exploits the regions around the best solutions. To evaluate the performance of IMI-GWO, a set of 16 integer and mixed-integer problems and two engineering application problems, namely gear train and pressure vessel design problems, have been considered. The performance of the IMI-GWO is compared with other algorithms which are applied to solve these problems in the literature and with some recent algorithms. The comparison illustrates the better performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

• This paper proposes a new advanced whale optimization algorithm for global optimization. • Levy flight and chaotic local search are integrated with whale optimization algorithm. • The effectiveness was proved by the extensive results on benchmark problems and real engineering design problems. In this study, two novel effective strategies composed of Lévy flight and chaotic local search are synchronously introduced into the whale optimization algorithm (WOA) to guide the swarm and further promote the harmony between the inclusive exploratory and neighborhood-informed capacities of the conventional technique and investigate the core searching capabilities of WOA in dealing with optimization tasks. However, the conventional WOA may simply be stuck at local optima or the global best may not be obtained successfully when tackling more complex optimization landscapes, including the multimodal and high dimensional scenarios. To substantiate the efficacy of the enhanced method, it is compared to a set of well-regarded variants of particle swarm optimization and differential evolution. The used benchmark problems are composed of unimodal, multimodal, and fixed-dimensions multimodal functions. Additionally, the proposed balanced method is applied to realize three practical, well-known mathematical models such as tension/compression spring, welded beam, pressure vessel design, three-bar truss design, and I-beam design problems. The experimental results and analysis reveal that the proposed algorithm can outperform other competitors in terms of the convergence speed and the quality of solutions. Promisingly, the proposed method can be treated as an effective and efficient auxiliary tool for more complex optimization models and scenarios. Image, graphical abstract [ABSTRACT FROM AUTHOR]

Abstract This study proposes an improved version of the Symbiotic Organisms Search (SOS) algorithm called Quasi-Oppositional Chaotic Symbiotic Organisms Search (QOCSOS). This improved algorithm integrated Quasi-Opposition-Based Learning (QOBL) and Chaotic Local Search (CLS) strategies with SOS for a better quality solution and faster convergence. To demonstrate and validate the new algorithm's effectiveness, the authors tested QOCSOS with twenty-six mathematical benchmark functions of different types and dimensions. In addition, QOCSOS optimized placements for distributed generation (DG) units in radial distribution networks and solved five structural design optimization problems, as practical optimization problems challenges. Comparative results showed that QOCSOS provided more accurate solutions than SOS and other methods, suggesting viability in dealing with global optimization problems. Graphical abstract Highlights • An improved algorithm called Quasi-Oppositional Chaotic Symbiotic Organisms Search (QOCSOS) is presented for solving global optimization problems. • QOCSOS is tested on twenty-six mathematical benchmark functions. • QOCSOS is applied to solve an optimal DG placement problem in radial distribution networks. • QOCSOS is applied to solve five engineering design problems. • For all application, QOCSOS achieved better solution quality than other methods. [ABSTRACT FROM AUTHOR]

Brain storm optimization (BSO) is a newly proposed optimization algorithm inspired by human being brainstorming process. After its appearance, much attention has been paid on and many attempts to improve its performance have been made. The search ability of BSO has been enhanced, but it still suffers from sticking into stagnation during exploitation phase. This paper proposes a novel method which incorporates BSO with chaotic local search (CLS) with the purpose of alleviating this situation. Chaos has properties of randomicity and ergodicity. These properties ensure CLS can explore every state of the search space if the search time duration is long enough. The incorporation of CLS can make BSO break the stagnation and keep the population’s diversity simultaneously, thus realizing a better balance between exploration and exploitation. Twelve chaotic maps are randomly selected for increasing the diversity of the search mechanism. Experimental and statistical results based on 25 benchmark functions demonstrate the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

This paper proposes a novel quasi-oppositional chaotic antlion optimizer (ALO) (QOCALO) for solving global optimization problems. ALO is a population based algorithm motivated by the unique hunting behavior of antlions in nature and exhibits strong influence in solving global and engineering optimization problems. In the proposed QOCALO algorithm of the present work, the initial population is generated using the quasi-opposition based learning (QOBL) and the concept of QOBL based generation jumping is utilized inside the main searching strategy of the proposed algorithm. Utilization of QOBL ensures better convergence speed of the proposed algorithm and it also provides better exploration of the search space. Alongside the QOBL, a chaotic local search (CLS) is also incorporated in the proposed QOCALO algorithm. The CLS guides local search around the global best solution that provides better exploitation of the search space. Thus, a better trade-off between exploration and exploitation holds for the proposed algorithm which makes it robust. It is observed that the proposed algorithm offers better results than the original ALO in terms of solution quality and convergence speed. The proposed QOCALO algorithm is implemented and tested, successfully, on nineteen mathematical benchmark test functions of varying complexities and the experimental results are compared to those offered by the basic ALO and some other recently developed nature inspired algorithms. The efficacy of the proposed algorithm is further utilized to solve three real world engineering optimization problems viz. (a) the placement and sizing problem of distributed generators in radial distribution networks, (b) the congestion management problem in power transmission system and (c) the optimal design of pressure vessel. [ABSTRACT FROM AUTHOR]

The search for clean energy alternatives to fossil fuels has been a major effort by researchers all over the world. Wind energy is one of the most optimal choices because of its cleanliness and renewability. However, the existence of the wake effect leads to a decrease in conversion efficiency. Finding the best wind turbine layout has become an important factor in the wind power generation system. Inspired by the excellent optimization capability of meta-heuristic algorithms, they are increasingly applied to solve complex constraints and design objectives in the wind farm layout optimization problems. It is reported that LSHADE, which is an advanced variant of differential evolution, provides a more efficient configuration of wind turbines than other meta-heuristic algorithms. This motivates us to conduct research in this direction and design an effective meta-heuristic algorithm with a chaotic local search strategy and an enhanced memory storage mechanism, which contributes to the reduction of global carbon emissions. The proposed new algorithm is called CLSHADE. The validity of the proposed algorithm is verified by the simulation of different constraints and wind field distribution profiles. Compared to four state-of-the-art meta-heuristic algorithms, the average conversion rate of the proposed algorithm is 92.87%, 89.13%, and 96.86% for three wind distribution profiles, respectively. The results show that the proposed algorithm has superiorities and effectiveness in wind farm layout optimization. • We develop a novel LSHADE variant for WFLO problems. • We analyze the effect of different complex wind profiles. • We consider different constraints for WFLO problems. • The numerical results indicate that the variant significantly outperforms its peers. [ABSTRACT FROM AUTHOR]

Accurate parameter identification of pump turbine governing system (PTGS) is of great importance to the precise modeling of pumped storage unit. As PTGS is characterized by uncertainties and strong nonlinear characteristics, it is difficult to identify its parameters. To solve the parameter identification problem for PTGS, an improved backtracking search algorithm (IBSA) is proposed by combining the original BSA with the orthogonal initialization technique, the chaotic local search operator, the elastic boundary processing strategy and the adaptive mutation scale factor. The proposed IBSA algorithm for parameter identification of PTGS was applied on an illustrative example to demonstrate its accuracy and efficiency. The simulation results have shown that IBSA performed better compared with the particle swarm optimization, the gravitational search algorithm and the original BSA in regard to solution quality and parameter identification accuracy.

With the total installed capacity of small hydro generator cluster (SHGC) continues to grow rapidly, the effect of SHGC will have affected to the security and stability of the main network in China, especially to power oscillations of passageway of ultra‐high‐voltage AC and DC. Thus, a precise equivalent model of SHGC is proposed in this study. In the proposed model, the fifth‐order model of generator is adopted as the equivalent generator model, the excitation system is simplified into proportional feedback control model and exponential load model is used as equivalent load model. In addition, Grey Wolf optimiser with chaotic local search is designed to identify the parameters of the equivalent model. Finally, the proposed equivalent technique is applied in one simulation experiment and one practical experiment. The experimental results demonstrate that the validity and accuracy of the proposed equivalent model and identification method in solving dynamic equivalent of SHGC in engineering practice. [ABSTRACT FROM AUTHOR]

Text clustering is widely used to create clusters of the digital documents. Selection of cluster centers plays an important role in the clustering. In this paper, we use artificial bee colony algorithm (ABC) to select appropriate cluster centers for creating clusters of the text documents. The ABC is a population-based nature-inspired algorithm, which simulates intelligent foraging behavior of the real honey bees and has been shown effective in solving many search and optimization problems. However, a major drawback of the algorithm is that it provides a good exploration of the search space at the cost of exploitation. In this paper, we improve search equation of the ABC and embed two local search paradigms namely chaotic local search and gradient search in the basic ABC to improve its exploitation capability. The proposed algorithm is named as chaotic gradient artificial bee colony. The effectiveness of the proposed algorithm is tested on three different benchmark text datasets namely Reuters-21,578, Classic4, and WebKB. The obtained results are compared with the ABC, a recent variant of the ABC namely gbest-guided ABC, a variant of the proposed methodology namely chaotic artificial bee colony, memetic ABC, and conventional clustering algorithm K-means. The empirical evaluation reveals very encouraging results in terms of the quality of solution and convergence speed. [ABSTRACT FROM AUTHOR]