In order to solve the problem that the krill herd(KH)algorithm is premature due to the decrease of population diversity,a new hybrid vortex search KH(VSKH)algorithm has been developed to deal with the global optimizat...In order to solve the problem that the krill herd(KH)algorithm is premature due to the decrease of population diversity,a new hybrid vortex search KH(VSKH)algorithm has been developed to deal with the global optimization problem.The improvement is that a new hybrid vortex search(HVS)operator is added into the updating process of the krill for the purpose of dealing with optimization problems more efficiently.Using 20 benchmark functions for comparison experiments,the results show that the VSKH algorithm has higher accuracy.展开更多
The Tunicate Swarm Algorithm(TSA)inspires by simulating the lives of Tunicates at sea and how food is obtained.This algorithm is easily entrapped to local optimization despite the simplicity and optimal,leading to ear...The Tunicate Swarm Algorithm(TSA)inspires by simulating the lives of Tunicates at sea and how food is obtained.This algorithm is easily entrapped to local optimization despite the simplicity and optimal,leading to early convergence compared to some metaheuristic algorithms.This paper sought to improve this algorithm's performance using mutating operators such as the lévy mutation operator,the Cauchy mutation operator,and the Gaussian mutation operator for global optimization problems.Thus,we introduced a version of this algorithm called the QLGCTSA algorithm.Each of these operators has a different performance,increasing the QLGCTSA algorithm performance at a specific optimization operation stage.This algorithm has been run on benchmark functions,including three different compositions,unimodal(UM),and multimodal(MM)groups and its performance evaluate six large-scale engineering problems.Experimental results show that the QLGCTSA algorithm had outperformed other competing optimization algorithms.展开更多
This paper proposes an enhanced arithmetic optimization algorithm(AOA)called PSAOA that incorporates the proposed probabilistic search strategy to increase the searching quality of the original AOA.Furthermore,an adju...This paper proposes an enhanced arithmetic optimization algorithm(AOA)called PSAOA that incorporates the proposed probabilistic search strategy to increase the searching quality of the original AOA.Furthermore,an adjustable parameter is also developed to balance the exploration and exploitation operations.In addition,a jump mechanism is included in the PSAOAto assist individuals in jumping out of local optima.Using 29 classical benchmark functions,the proposed PSAOA is extensively tested.Compared to the AOA and other well-known methods,the experiments demonstrated that the proposed PSAOA beats existing comparison algorithms on the majority of the test functions.展开更多
A novel heuristic search algorithm called seeker op- timization algorithm （SOA） is proposed for the real-parameter optimization. The proposed SOA is based on simulating the act of human searching. In the SOA, search...A novel heuristic search algorithm called seeker op- timization algorithm （SOA） is proposed for the real-parameter optimization. The proposed SOA is based on simulating the act of human searching. In the SOA, search direction is based on empir- ical gradients by evaluating the response to the position changes, while step length is based on uncertainty reasoning by using a simple fuzzy rule. The effectiveness of the SOA is evaluated by using a challenging set of typically complex functions in compari- son to differential evolution （DE） and three modified particle swarm optimization （PSO） algorithms. The simulation results show that the performance of the SOA is superior or comparable to that of the other algorithms.展开更多
Crow Search Algorithm(CSA)is a swarm-based single-objective optimizer proposed in recent years,which tried to inspire the behavior of crows that hide foods in different locations and retrieve them when needed.The orig...Crow Search Algorithm(CSA)is a swarm-based single-objective optimizer proposed in recent years,which tried to inspire the behavior of crows that hide foods in different locations and retrieve them when needed.The original version of the CSA has simple parameters and moderate performance.However,it often tends to converge slowly or get stuck in a locally optimal region due to a missed harmonizing strategy during the exploitation and exploration phases.Therefore,strategies of mutation and crisscross are combined into CSA(CCMSCSA)in this paper to improve the performance and provide an efficient optimizer for various optimization problems.To verify the superiority of CCMSCSA,a set of comparisons has been performed reasonably with some well-established metaheuristics and advanced metaheuristics on 15 benchmark functions.The experimental results expose and verify that the proposed CCMSCSA has meaningfully improved the convergence speed and the ability to jump out of the local optimum.In addition,the scalability of CCMSCSA is analyzed,and the algorithm is applied to several engineering problems in a constrained space and feature selection problems.Experimental results show that the scalability of CCMSCSA has been significantly improved and can find better solutions than its competitors when dealing with combinatorial optimization problems.The proposed CCMSCSA performs well in almost all experimental results.Therefore,we hope the researchers can see it as an effective method for solving constrained and unconstrained optimization problems.展开更多
A simplified group search optimizer algorithm denoted as"SGSO"for large scale global optimization is presented in this paper to obtain a simple algorithm with superior performance on high-dimensional problem...A simplified group search optimizer algorithm denoted as"SGSO"for large scale global optimization is presented in this paper to obtain a simple algorithm with superior performance on high-dimensional problems.The SGSO adopts an improved sharing strategy which shares information of not only the best member but also the other good members,and uses a simpler search method instead of searching by the head angle.Furthermore,the SGSO increases the percentage of scroungers to accelerate convergence speed.Compared with genetic algorithm(GA),particle swarm optimizer(PSO)and group search optimizer(GSO),SGSO is tested on seven benchmark functions with dimensions 30,100,500 and 1 000.It can be concluded that the SGSO has a remarkably superior performance to GA,PSO and GSO for large scale global optimization.展开更多
This article addresses the issues of falling into local optima and insufficient exploration capability in the Arithmetic Optimization Algorithm (AOA), proposing an improved Arithmetic Optimization Algorithm with a mul...This article addresses the issues of falling into local optima and insufficient exploration capability in the Arithmetic Optimization Algorithm (AOA), proposing an improved Arithmetic Optimization Algorithm with a multi-strategy mechanism (BSFAOA). This algorithm introduces three strategies within the standard AOA framework: an adaptive balance factor SMOA based on sine functions, a search strategy combining Spiral Search and Brownian Motion, and a hybrid perturbation strategy based on Whale Fall Mechanism and Polynomial Differential Learning. The BSFAOA algorithm is analyzed in depth on the well-known 23 benchmark functions, CEC2019 test functions, and four real optimization problems. The experimental results demonstrate that the BSFAOA algorithm can better balance the exploration and exploitation capabilities, significantly enhancing the stability, convergence mode, and search efficiency of the AOA algorithm.展开更多
The sparrow search algorithm(SSA)is a newly proposed meta-heuristic optimization algorithm based on the sparrowforaging principle.Similar to other meta-heuristic algorithms,SSA has problems such as slowconvergence spe...The sparrow search algorithm(SSA)is a newly proposed meta-heuristic optimization algorithm based on the sparrowforaging principle.Similar to other meta-heuristic algorithms,SSA has problems such as slowconvergence speed and difficulty in jumping out of the local optimum.In order to overcome these shortcomings,a chaotic sparrow search algorithm based on logarithmic spiral strategy and adaptive step strategy(CLSSA)is proposed in this paper.Firstly,in order to balance the exploration and exploitation ability of the algorithm,chaotic mapping is introduced to adjust the main parameters of SSA.Secondly,in order to improve the diversity of the population and enhance the search of the surrounding space,the logarithmic spiral strategy is introduced to improve the sparrow search mechanism.Finally,the adaptive step strategy is introduced to better control the process of algorithm exploitation and exploration.The best chaotic map is determined by different test functions,and the CLSSA with the best chaotic map is applied to solve 23 benchmark functions and 3 classical engineering problems.The simulation results show that the iterative map is the best chaotic map,and CLSSA is efficient and useful for engineering problems,which is better than all comparison algorithms.展开更多
During the last decade, many variants of the original particle swarm optimization （PSO） algorithm have been proposed for global numerical optimization, hut they usually face many challenges such as low solution qual...During the last decade, many variants of the original particle swarm optimization （PSO） algorithm have been proposed for global numerical optimization, hut they usually face many challenges such as low solution quality and slow convergence speed on multimodal function optimization. A composite particle swarm optimization （CPSO） for solving these difficulties is presented, in which a novel learning strategy plus an assisted search mechanism framework is used. Instead of simple learning strategy of the original PSO, the proposed CPSO combines one particle＇s historical best information and the global best information into one learning exemplar to guide the particle movement. The proposed learning strategy can reserve the original search information and lead to faster convergence speed. The proposed assisted search mechanism is designed to look for the global optimum. Search direction of particles can be greatly changed by this mechanism so that the algorithm has a large chance to escape from local optima. In order to make the assisted search mechanism more efficient and the algorithm more reliable, the executive probability of the assisted search mechanism is adjusted by the feedback of the improvement degree of optimal value after each iteration. According to the result of numerical experiments on multimodal benchmark functions such as Schwefel, Rastrigin, Ackley and Griewank both with and without coordinate rotation, the proposed CPSO offers faster convergence speed, higher quality solution and stronger robustness than other variants of PSO.展开更多
A new hybrid optimization algorithm was presented by integrating the gravitational search algorithm （GSA） with the sequential quadratic programming （SQP）, namely GSA-SQP, for solving global optimization problems a...A new hybrid optimization algorithm was presented by integrating the gravitational search algorithm （GSA） with the sequential quadratic programming （SQP）, namely GSA-SQP, for solving global optimization problems and minimization of factor of safety in slope stability analysis. The new algorithm combines the global exploration ability of the GSA to converge rapidly to a near optimum solution. In addition, it uses the accurate local exploitation ability of the SQP to accelerate the search process and find an accurate solution. A set of five well-known benchmark optimization problems was used to validate the performance of the GSA-SQP as a global optimization algorithm and facilitate comparison with the classical GSA. In addition, the effectiveness of the proposed method for slope stability analysis was investigated using three ease studies of slope stability problems from the literature. The factor of safety of earth slopes was evaluated using the Morgenstern-Price method. The numerical experiments demonstrate that the hybrid algorithm converges faster to a significantly more accurate final solution for a variety of benchmark test functions and slope stability problems.展开更多
In this paper, we develop a new theoretical framework by means of the absorbing Markov process theory for analyzing some stochastic global optimization algorithms. Applying the framework to the pure random search, we ...In this paper, we develop a new theoretical framework by means of the absorbing Markov process theory for analyzing some stochastic global optimization algorithms. Applying the framework to the pure random search, we prove that the pure random search converges to the global minimum in probability and its time has geometry distribution. We also analyze the pure adaptive search by this framework and turn out that the pure adaptive search converges to the global minimum in probability and its time has Poisson distribution.展开更多
Feature selection(FS)is an adequate data pre-processing method that reduces the dimensionality of datasets and is used in bioinformatics,finance,and medicine.Traditional FS approaches,however,frequently struggle to id...Feature selection(FS)is an adequate data pre-processing method that reduces the dimensionality of datasets and is used in bioinformatics,finance,and medicine.Traditional FS approaches,however,frequently struggle to identify the most important characteristics when dealing with high-dimensional information.To alleviate the imbalance of explore search ability and exploit search ability of the Whale Optimization Algorithm(WOA),we propose an enhanced WOA,namely SCLWOA,that incorporates sine chaos and comprehensive learning(CL)strategies.Among them,the CL mechanism contributes to improving the ability to explore.At the same time,the sine chaos is used to enhance the exploitation capacity and help the optimizer to gain a better initial solution.The hybrid performance of SCLWOA was evaluated comprehensively on IEEE CEC2017 test functions,including its qualitative analysis and comparisons with other optimizers.The results demonstrate that SCLWOA is superior to other algorithms in accuracy and converges faster than others.Besides,the variant of Binary SCLWOA(BSCLWOA)and other binary optimizers obtained by the mapping function was evaluated on 12 UCI data sets.Subsequently,BSCLWOA has proven very competitive in classification precision and feature reduction.展开更多
A modification of evolutionary programming or evolution strategies for ndimensional global optimization is proposed. Based on the ergodicity and inherentrandomness of chaos, the main characteristic of the new algorith...A modification of evolutionary programming or evolution strategies for ndimensional global optimization is proposed. Based on the ergodicity and inherentrandomness of chaos, the main characteristic of the new algorithm which includes two phases is that chaotic behavior is exploited to conduct a rough search of the problem space in order to find the promising individuals in Phase I. Adjustment strategy of steplength and intensive searches in Phase II are employed. The population sequences generated by the algorithm asymptotically converge to global optimal solutions with probability one. The proposed algorithm is applied to several typical test problems. Numerical results illustrate that this algorithm can more efficiently solve complex global optimization problems than evolutionary programming and evolution strategies in most cases.展开更多
A fast global convergence algorithm, small-world optimization (SWO), was designed to solve the global optimization problems, which was inspired from small-world theory and six degrees of separation principle in sociol...A fast global convergence algorithm, small-world optimization (SWO), was designed to solve the global optimization problems, which was inspired from small-world theory and six degrees of separation principle in sociology. Firstly, the solution space was organized into a small-world network model based on social relationship network. Secondly, a simple search strategy was adopted to navigate into this network in order to realize the optimization. In SWO, the two operators for searching the short-range contacts and long-range contacts in small-world network were corresponding to the exploitation and exploration, which have been revealed as the common features in many intelligent algorithms. The proposed algorithm was validated via popular benchmark functions and engineering problems. And also the impacts of parameters were studied. The simulation results indicate that because of the small-world theory, it is suitable for heuristic methods to search targets efficiently in this constructed small-world network model. It is not easy for each test mail to fall into a local trap by shifting into two mapping spaces in order to accelerate the convergence speed. Compared with some classical algorithms, SWO is inherited with optimal features and outstanding in convergence speed. Thus, the algorithm can be considered as a good alternative to solve global optimization problems.展开更多
In this paper, the improvement of pure random search is studied. By taking some information of the function to be minimized into consideration, the authors propose two stochastic global optimization algorithms. Some n...In this paper, the improvement of pure random search is studied. By taking some information of the function to be minimized into consideration, the authors propose two stochastic global optimization algorithms. Some numerical experiments for the new stochastic global optimization algorithms are presented for a class of test problems.展开更多
Hybridizing metaheuristic algorithms involves synergistically combining different optimization techniques to effectively address complex and challenging optimization problems.This approach aims to leverage the strengt...Hybridizing metaheuristic algorithms involves synergistically combining different optimization techniques to effectively address complex and challenging optimization problems.This approach aims to leverage the strengths of multiple algorithms,enhancing solution quality,convergence speed,and robustness,thereby offering a more versatile and efficient means of solving intricate real-world optimization tasks.In this paper,we introduce a hybrid algorithm that amalgamates three distinct metaheuristics:the Beluga Whale Optimization(BWO),the Honey Badger Algorithm(HBA),and the Jellyfish Search(JS)optimizer.The proposed hybrid algorithm will be referred to as BHJO.Through this fusion,the BHJO algorithm aims to leverage the strengths of each optimizer.Before this hybridization,we thoroughly examined the exploration and exploitation capabilities of the BWO,HBA,and JS metaheuristics,as well as their ability to strike a balance between exploration and exploitation.This meticulous analysis allowed us to identify the pros and cons of each algorithm,enabling us to combine them in a novel hybrid approach that capitalizes on their respective strengths for enhanced optimization performance.In addition,the BHJO algorithm incorporates Opposition-Based Learning(OBL)to harness the advantages offered by this technique,leveraging its diverse exploration,accelerated convergence,and improved solution quality to enhance the overall performance and effectiveness of the hybrid algorithm.Moreover,the performance of the BHJO algorithm was evaluated across a range of both unconstrained and constrained optimization problems,providing a comprehensive assessment of its efficacy and applicability in diverse problem domains.Similarly,the BHJO algorithm was subjected to a comparative analysis with several renowned algorithms,where mean and standard deviation values were utilized as evaluation metrics.This rigorous comparison aimed to assess the performance of the BHJOalgorithmabout its counterparts,shedding light on its effectiveness and reliability in solving optimization problems.Finally,the obtained numerical statistics underwent rigorous analysis using the Friedman post hoc Dunn’s test.The resulting numerical values revealed the BHJO algorithm’s competitiveness in tackling intricate optimization problems,affirming its capability to deliver favorable outcomes in challenging scenarios.展开更多
This paper presents an improved gravitational search algorithm （IGSA） as a hybridization of a relatively recent evolutionary algorithm called gravitational search algorithm （GSA）, with the free search differential...This paper presents an improved gravitational search algorithm （IGSA） as a hybridization of a relatively recent evolutionary algorithm called gravitational search algorithm （GSA）, with the free search differential evolution （FSDE）. This combination incorporates FSDE into the optimization process of GSA with an attempt to avoid the premature convergence in GSA. This strategy makes full use of the exploration ability of GSA and the exploitation ability of FSDE. IGSA is tested on a suite of benchmark functions. The experimental results demonstrate the good performance of IGSA.展开更多
In this paper a hybrid algorithm which combines the pattern search method and the genetic algorithm for unconstrained optimization is presented. The algorithm is a deterministic pattern search algorithm,but in the sea...In this paper a hybrid algorithm which combines the pattern search method and the genetic algorithm for unconstrained optimization is presented. The algorithm is a deterministic pattern search algorithm,but in the search step of pattern search algorithm,the trial points are produced by a way like the genetic algorithm. At each iterate, by reduplication,crossover and mutation, a finite set of points can be used. In theory,the algorithm is globally convergent. The most stir is the numerical results showing that it can find the global minimizer for some problems ,which other pattern search algorithms don＇t bear.展开更多
In view of the serious reactive power loss in the rural network, improved ant colony optimization algorithm （ACOA） was used to optimize the reactive power compensation for the rural distribution system. In this stud...In view of the serious reactive power loss in the rural network, improved ant colony optimization algorithm （ACOA） was used to optimize the reactive power compensation for the rural distribution system. In this study, the traditional ACOA was improved in two aspects： one was the local search strategy, and the other was pheromone mutation and re-initialization strategies. The reactive power optimization for a county＇s distribution network showed that the improved ACOA was practicable.展开更多
基金Supported by the National Natural Science Foundation of China(11871383,71471140)。
文摘In order to solve the problem that the krill herd(KH)algorithm is premature due to the decrease of population diversity,a new hybrid vortex search KH(VSKH)algorithm has been developed to deal with the global optimization problem.The improvement is that a new hybrid vortex search(HVS)operator is added into the updating process of the krill for the purpose of dealing with optimization problems more efficiently.Using 20 benchmark functions for comparison experiments,the results show that the VSKH algorithm has higher accuracy.
文摘The Tunicate Swarm Algorithm(TSA)inspires by simulating the lives of Tunicates at sea and how food is obtained.This algorithm is easily entrapped to local optimization despite the simplicity and optimal,leading to early convergence compared to some metaheuristic algorithms.This paper sought to improve this algorithm's performance using mutating operators such as the lévy mutation operator,the Cauchy mutation operator,and the Gaussian mutation operator for global optimization problems.Thus,we introduced a version of this algorithm called the QLGCTSA algorithm.Each of these operators has a different performance,increasing the QLGCTSA algorithm performance at a specific optimization operation stage.This algorithm has been run on benchmark functions,including three different compositions,unimodal(UM),and multimodal(MM)groups and its performance evaluate six large-scale engineering problems.Experimental results show that the QLGCTSA algorithm had outperformed other competing optimization algorithms.
基金partially supported by the Fundamental Research Funds for the Central Universities(WUT:2022IVA067)the National Natural Science Foundation of China(Grant No.:72172112)the Fundamental Research Funds for the Central Universities(HUST:2019kfyRCPY038).
文摘This paper proposes an enhanced arithmetic optimization algorithm(AOA)called PSAOA that incorporates the proposed probabilistic search strategy to increase the searching quality of the original AOA.Furthermore,an adjustable parameter is also developed to balance the exploration and exploitation operations.In addition,a jump mechanism is included in the PSAOAto assist individuals in jumping out of local optima.Using 29 classical benchmark functions,the proposed PSAOA is extensively tested.Compared to the AOA and other well-known methods,the experiments demonstrated that the proposed PSAOA beats existing comparison algorithms on the majority of the test functions.
基金supported by the National Natural Science Foundation of China(60870004)
文摘A novel heuristic search algorithm called seeker op- timization algorithm （SOA） is proposed for the real-parameter optimization. The proposed SOA is based on simulating the act of human searching. In the SOA, search direction is based on empir- ical gradients by evaluating the response to the position changes, while step length is based on uncertainty reasoning by using a simple fuzzy rule. The effectiveness of the SOA is evaluated by using a challenging set of typically complex functions in compari- son to differential evolution （DE） and three modified particle swarm optimization （PSO） algorithms. The simulation results show that the performance of the SOA is superior or comparable to that of the other algorithms.
基金Natural Science Foundation of Zhejiang Province(LZ22F020005)National Natural Science Foundation of China(42164002,62076185 and,U1809209)National Key R&D Program of China(2018YFC1503806).
文摘Crow Search Algorithm(CSA)is a swarm-based single-objective optimizer proposed in recent years,which tried to inspire the behavior of crows that hide foods in different locations and retrieve them when needed.The original version of the CSA has simple parameters and moderate performance.However,it often tends to converge slowly or get stuck in a locally optimal region due to a missed harmonizing strategy during the exploitation and exploration phases.Therefore,strategies of mutation and crisscross are combined into CSA(CCMSCSA)in this paper to improve the performance and provide an efficient optimizer for various optimization problems.To verify the superiority of CCMSCSA,a set of comparisons has been performed reasonably with some well-established metaheuristics and advanced metaheuristics on 15 benchmark functions.The experimental results expose and verify that the proposed CCMSCSA has meaningfully improved the convergence speed and the ability to jump out of the local optimum.In addition,the scalability of CCMSCSA is analyzed,and the algorithm is applied to several engineering problems in a constrained space and feature selection problems.Experimental results show that the scalability of CCMSCSA has been significantly improved and can find better solutions than its competitors when dealing with combinatorial optimization problems.The proposed CCMSCSA performs well in almost all experimental results.Therefore,we hope the researchers can see it as an effective method for solving constrained and unconstrained optimization problems.
基金the Science and Technology Planning Project of Hunan Province(No.2011TP4016-3)the Construct Program of the Key Discipline(Technology of Computer Application)in Xiangnan University
文摘A simplified group search optimizer algorithm denoted as"SGSO"for large scale global optimization is presented in this paper to obtain a simple algorithm with superior performance on high-dimensional problems.The SGSO adopts an improved sharing strategy which shares information of not only the best member but also the other good members,and uses a simpler search method instead of searching by the head angle.Furthermore,the SGSO increases the percentage of scroungers to accelerate convergence speed.Compared with genetic algorithm(GA),particle swarm optimizer(PSO)and group search optimizer(GSO),SGSO is tested on seven benchmark functions with dimensions 30,100,500 and 1 000.It can be concluded that the SGSO has a remarkably superior performance to GA,PSO and GSO for large scale global optimization.
文摘This article addresses the issues of falling into local optima and insufficient exploration capability in the Arithmetic Optimization Algorithm (AOA), proposing an improved Arithmetic Optimization Algorithm with a multi-strategy mechanism (BSFAOA). This algorithm introduces three strategies within the standard AOA framework: an adaptive balance factor SMOA based on sine functions, a search strategy combining Spiral Search and Brownian Motion, and a hybrid perturbation strategy based on Whale Fall Mechanism and Polynomial Differential Learning. The BSFAOA algorithm is analyzed in depth on the well-known 23 benchmark functions, CEC2019 test functions, and four real optimization problems. The experimental results demonstrate that the BSFAOA algorithm can better balance the exploration and exploitation capabilities, significantly enhancing the stability, convergence mode, and search efficiency of the AOA algorithm.
基金The Science Foundation of Shanxi Province,China(2020JQ-481,2021JM-224)Aero Science Foundation of China(201951096002).
文摘The sparrow search algorithm(SSA)is a newly proposed meta-heuristic optimization algorithm based on the sparrowforaging principle.Similar to other meta-heuristic algorithms,SSA has problems such as slowconvergence speed and difficulty in jumping out of the local optimum.In order to overcome these shortcomings,a chaotic sparrow search algorithm based on logarithmic spiral strategy and adaptive step strategy(CLSSA)is proposed in this paper.Firstly,in order to balance the exploration and exploitation ability of the algorithm,chaotic mapping is introduced to adjust the main parameters of SSA.Secondly,in order to improve the diversity of the population and enhance the search of the surrounding space,the logarithmic spiral strategy is introduced to improve the sparrow search mechanism.Finally,the adaptive step strategy is introduced to better control the process of algorithm exploitation and exploration.The best chaotic map is determined by different test functions,and the CLSSA with the best chaotic map is applied to solve 23 benchmark functions and 3 classical engineering problems.The simulation results show that the iterative map is the best chaotic map,and CLSSA is efficient and useful for engineering problems,which is better than all comparison algorithms.
基金Projects(50275150,61173052)supported by the National Natural Science Foundation of China
文摘During the last decade, many variants of the original particle swarm optimization （PSO） algorithm have been proposed for global numerical optimization, hut they usually face many challenges such as low solution quality and slow convergence speed on multimodal function optimization. A composite particle swarm optimization （CPSO） for solving these difficulties is presented, in which a novel learning strategy plus an assisted search mechanism framework is used. Instead of simple learning strategy of the original PSO, the proposed CPSO combines one particle＇s historical best information and the global best information into one learning exemplar to guide the particle movement. The proposed learning strategy can reserve the original search information and lead to faster convergence speed. The proposed assisted search mechanism is designed to look for the global optimum. Search direction of particles can be greatly changed by this mechanism so that the algorithm has a large chance to escape from local optima. In order to make the assisted search mechanism more efficient and the algorithm more reliable, the executive probability of the assisted search mechanism is adjusted by the feedback of the improvement degree of optimal value after each iteration. According to the result of numerical experiments on multimodal benchmark functions such as Schwefel, Rastrigin, Ackley and Griewank both with and without coordinate rotation, the proposed CPSO offers faster convergence speed, higher quality solution and stronger robustness than other variants of PSO.
文摘A new hybrid optimization algorithm was presented by integrating the gravitational search algorithm （GSA） with the sequential quadratic programming （SQP）, namely GSA-SQP, for solving global optimization problems and minimization of factor of safety in slope stability analysis. The new algorithm combines the global exploration ability of the GSA to converge rapidly to a near optimum solution. In addition, it uses the accurate local exploitation ability of the SQP to accelerate the search process and find an accurate solution. A set of five well-known benchmark optimization problems was used to validate the performance of the GSA-SQP as a global optimization algorithm and facilitate comparison with the classical GSA. In addition, the effectiveness of the proposed method for slope stability analysis was investigated using three ease studies of slope stability problems from the literature. The factor of safety of earth slopes was evaluated using the Morgenstern-Price method. The numerical experiments demonstrate that the hybrid algorithm converges faster to a significantly more accurate final solution for a variety of benchmark test functions and slope stability problems.
文摘In this paper, we develop a new theoretical framework by means of the absorbing Markov process theory for analyzing some stochastic global optimization algorithms. Applying the framework to the pure random search, we prove that the pure random search converges to the global minimum in probability and its time has geometry distribution. We also analyze the pure adaptive search by this framework and turn out that the pure adaptive search converges to the global minimum in probability and its time has Poisson distribution.
基金This work is supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2023R193)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.This work was supported in part by the Natural Science Foundation of Zhejiang Province(LZ22F020005)+4 种基金National Natural Science Foundation of China(62076185,U1809209)Natural Science Foundation of Zhejiang Province(LD21F020001,LZ22F020005)National Natural Science Foundation of China(62076185)Key Laboratory of Intelligent Image Processing and Analysis,Wenzhou,China(2021HZSY0071)Wenzhou Major Scientific and Technological Innovation Project(ZY2019020).
文摘Feature selection(FS)is an adequate data pre-processing method that reduces the dimensionality of datasets and is used in bioinformatics,finance,and medicine.Traditional FS approaches,however,frequently struggle to identify the most important characteristics when dealing with high-dimensional information.To alleviate the imbalance of explore search ability and exploit search ability of the Whale Optimization Algorithm(WOA),we propose an enhanced WOA,namely SCLWOA,that incorporates sine chaos and comprehensive learning(CL)strategies.Among them,the CL mechanism contributes to improving the ability to explore.At the same time,the sine chaos is used to enhance the exploitation capacity and help the optimizer to gain a better initial solution.The hybrid performance of SCLWOA was evaluated comprehensively on IEEE CEC2017 test functions,including its qualitative analysis and comparisons with other optimizers.The results demonstrate that SCLWOA is superior to other algorithms in accuracy and converges faster than others.Besides,the variant of Binary SCLWOA(BSCLWOA)and other binary optimizers obtained by the mapping function was evaluated on 12 UCI data sets.Subsequently,BSCLWOA has proven very competitive in classification precision and feature reduction.
文摘A modification of evolutionary programming or evolution strategies for ndimensional global optimization is proposed. Based on the ergodicity and inherentrandomness of chaos, the main characteristic of the new algorithm which includes two phases is that chaotic behavior is exploited to conduct a rough search of the problem space in order to find the promising individuals in Phase I. Adjustment strategy of steplength and intensive searches in Phase II are employed. The population sequences generated by the algorithm asymptotically converge to global optimal solutions with probability one. The proposed algorithm is applied to several typical test problems. Numerical results illustrate that this algorithm can more efficiently solve complex global optimization problems than evolutionary programming and evolution strategies in most cases.
基金Projects(51105157, 50875101) supported by the National Natural Science Foundation of ChinaProject(2009AA043301) supported by the National High Technology Research and Development Program of China
文摘A fast global convergence algorithm, small-world optimization (SWO), was designed to solve the global optimization problems, which was inspired from small-world theory and six degrees of separation principle in sociology. Firstly, the solution space was organized into a small-world network model based on social relationship network. Secondly, a simple search strategy was adopted to navigate into this network in order to realize the optimization. In SWO, the two operators for searching the short-range contacts and long-range contacts in small-world network were corresponding to the exploitation and exploration, which have been revealed as the common features in many intelligent algorithms. The proposed algorithm was validated via popular benchmark functions and engineering problems. And also the impacts of parameters were studied. The simulation results indicate that because of the small-world theory, it is suitable for heuristic methods to search targets efficiently in this constructed small-world network model. It is not easy for each test mail to fall into a local trap by shifting into two mapping spaces in order to accelerate the convergence speed. Compared with some classical algorithms, SWO is inherited with optimal features and outstanding in convergence speed. Thus, the algorithm can be considered as a good alternative to solve global optimization problems.
文摘In this paper, the improvement of pure random search is studied. By taking some information of the function to be minimized into consideration, the authors propose two stochastic global optimization algorithms. Some numerical experiments for the new stochastic global optimization algorithms are presented for a class of test problems.
基金funded by the Researchers Supporting Program at King Saud University(RSPD2024R809).
文摘Hybridizing metaheuristic algorithms involves synergistically combining different optimization techniques to effectively address complex and challenging optimization problems.This approach aims to leverage the strengths of multiple algorithms,enhancing solution quality,convergence speed,and robustness,thereby offering a more versatile and efficient means of solving intricate real-world optimization tasks.In this paper,we introduce a hybrid algorithm that amalgamates three distinct metaheuristics:the Beluga Whale Optimization(BWO),the Honey Badger Algorithm(HBA),and the Jellyfish Search(JS)optimizer.The proposed hybrid algorithm will be referred to as BHJO.Through this fusion,the BHJO algorithm aims to leverage the strengths of each optimizer.Before this hybridization,we thoroughly examined the exploration and exploitation capabilities of the BWO,HBA,and JS metaheuristics,as well as their ability to strike a balance between exploration and exploitation.This meticulous analysis allowed us to identify the pros and cons of each algorithm,enabling us to combine them in a novel hybrid approach that capitalizes on their respective strengths for enhanced optimization performance.In addition,the BHJO algorithm incorporates Opposition-Based Learning(OBL)to harness the advantages offered by this technique,leveraging its diverse exploration,accelerated convergence,and improved solution quality to enhance the overall performance and effectiveness of the hybrid algorithm.Moreover,the performance of the BHJO algorithm was evaluated across a range of both unconstrained and constrained optimization problems,providing a comprehensive assessment of its efficacy and applicability in diverse problem domains.Similarly,the BHJO algorithm was subjected to a comparative analysis with several renowned algorithms,where mean and standard deviation values were utilized as evaluation metrics.This rigorous comparison aimed to assess the performance of the BHJOalgorithmabout its counterparts,shedding light on its effectiveness and reliability in solving optimization problems.Finally,the obtained numerical statistics underwent rigorous analysis using the Friedman post hoc Dunn’s test.The resulting numerical values revealed the BHJO algorithm’s competitiveness in tackling intricate optimization problems,affirming its capability to deliver favorable outcomes in challenging scenarios.
基金supported by the National Natural Science Foundation of China (70871081)the Shanghai Leading Academic Discipline Project of China (S1205YLXK)
文摘This paper presents an improved gravitational search algorithm （IGSA） as a hybridization of a relatively recent evolutionary algorithm called gravitational search algorithm （GSA）, with the free search differential evolution （FSDE）. This combination incorporates FSDE into the optimization process of GSA with an attempt to avoid the premature convergence in GSA. This strategy makes full use of the exploration ability of GSA and the exploitation ability of FSDE. IGSA is tested on a suite of benchmark functions. The experimental results demonstrate the good performance of IGSA.
文摘In this paper a hybrid algorithm which combines the pattern search method and the genetic algorithm for unconstrained optimization is presented. The algorithm is a deterministic pattern search algorithm,but in the search step of pattern search algorithm,the trial points are produced by a way like the genetic algorithm. At each iterate, by reduplication,crossover and mutation, a finite set of points can be used. In theory,the algorithm is globally convergent. The most stir is the numerical results showing that it can find the global minimizer for some problems ,which other pattern search algorithms don＇t bear.
基金Supported by China Postdoctoral Science Foundation(20090460873)
文摘In view of the serious reactive power loss in the rural network, improved ant colony optimization algorithm （ACOA） was used to optimize the reactive power compensation for the rural distribution system. In this study, the traditional ACOA was improved in two aspects： one was the local search strategy, and the other was pheromone mutation and re-initialization strategies. The reactive power optimization for a county＇s distribution network showed that the improved ACOA was practicable.
