The challenge of optimising multimodal functions within high-dimensional domains constitutes a notable difficulty in evolutionary computation research.Addressing this issue,this study introduces the Deep Backtracking ...The challenge of optimising multimodal functions within high-dimensional domains constitutes a notable difficulty in evolutionary computation research.Addressing this issue,this study introduces the Deep Backtracking Bare-Bones Particle Swarm Optimisation(DBPSO)algorithm,an innovative approach built upon the integration of the Deep Memory Storage Mechanism(DMSM)and the Dynamic Memory Activation Strategy(DMAS).The DMSM enhances the memory retention for the globally optimal particle,promoting interaction between standard particles and their historically optimal counterparts.In parallel,DMAS assures the updated position of the globally optimal particle is appropriately aligned with the deep memory repository.The efficacy of DBPSO was rigorously assessed through a series of simulations employing the CEC2017 benchmark suite.A comparative analysis juxtaposed DBPSO's performance against five contemporary evolutionary algorithms across two experimental conditions:Dimension-50 and Dimension-100.In the 50D trials,DBPSO attained an average ranking of 2.03,whereas in the 100D scenarios,it improved to an average ranking of 1.9.Further examination utilising the CEC2019 benchmark functions revealed DBPSO's robustness,securing four first-place finishes,three second-place standings,and three third-place positions,culminating in an unmatched average ranking of 1.9 across all algorithms.These empirical results corroborate DBPSO's proficiency in delivering precise solutions for complex,high-dimensional optimisation challenges.展开更多
Complex optimization problems hold broad significance across numerous fields and applications.However,as the dimensionality of such problems increases,issues like the curse of dimensionality and local optima trapping ...Complex optimization problems hold broad significance across numerous fields and applications.However,as the dimensionality of such problems increases,issues like the curse of dimensionality and local optima trapping also arise.To address these challenges,this paper proposes a novel Wild Gibbon Optimization Algorithm(WGOA)based on an analysis of wild gibbon population behavior.WGOAcomprises two strategies:community search and community competition.The community search strategy facilitates information exchange between two gibbon families,generating multiple candidate solutions to enhance algorithm diversity.Meanwhile,the community competition strategy reselects leaders for the population after each iteration,thus enhancing algorithm precision.To assess the algorithm’s performance,CEC2017 and CEC2022 are chosen as test functions.In the CEC2017 test suite,WGOA secures first place in 10 functions.In the CEC2022 benchmark functions,WGOA obtained the first rank in 5 functions.The ultimate experimental findings demonstrate that theWildGibbonOptimization Algorithm outperforms others in tested functions.This underscores the strong robustness and stability of the gibbonalgorithm in tackling complex single-objective optimization problems.展开更多
Purpose–The purpose of this paper is to propose a fault-tolerant technology for increasing the durability of application programs when evolutionary computation is performed by fast parallel processing on many-core pr...Purpose–The purpose of this paper is to propose a fault-tolerant technology for increasing the durability of application programs when evolutionary computation is performed by fast parallel processing on many-core processors such as graphics processing units(GPUs)and multi-core processors(MCPs).Design/methodology/approach–For distributed genetic algorithm(GA)models,the paper proposes a method where an island’s ID number is added to the header of data transferred by this island for use in fault detection.Findings–The paper has shown that the processing time of the proposed idea is practically negligible in applications and also shown that an optimal solution can be obtained even with a single stuck-at fault or a transient fault,and that increasing the number of parallel threads makes the system less susceptible to faults.Originality/value–The study described in this paper is a new approach to increase the sustainability of application program using distributed GA on GPUs and MCPs.展开更多
基金supported by the Artificial Intelligence Innovation Project of Wuhan Science and Technology Bureau,2023010402040016the Natural Science Foundation of Hubei Province of China,2022CFB076,JSPS KAKENHI,JP25K15279,Natural Science Foundation of Hubei Province,2023AFB003+1 种基金the National Natural Science Foundation of China,52201363the Education Department Scientific Research Programme Project of Hubei Province of China,Q20222208.
文摘The challenge of optimising multimodal functions within high-dimensional domains constitutes a notable difficulty in evolutionary computation research.Addressing this issue,this study introduces the Deep Backtracking Bare-Bones Particle Swarm Optimisation(DBPSO)algorithm,an innovative approach built upon the integration of the Deep Memory Storage Mechanism(DMSM)and the Dynamic Memory Activation Strategy(DMAS).The DMSM enhances the memory retention for the globally optimal particle,promoting interaction between standard particles and their historically optimal counterparts.In parallel,DMAS assures the updated position of the globally optimal particle is appropriately aligned with the deep memory repository.The efficacy of DBPSO was rigorously assessed through a series of simulations employing the CEC2017 benchmark suite.A comparative analysis juxtaposed DBPSO's performance against five contemporary evolutionary algorithms across two experimental conditions:Dimension-50 and Dimension-100.In the 50D trials,DBPSO attained an average ranking of 2.03,whereas in the 100D scenarios,it improved to an average ranking of 1.9.Further examination utilising the CEC2019 benchmark functions revealed DBPSO's robustness,securing four first-place finishes,three second-place standings,and three third-place positions,culminating in an unmatched average ranking of 1.9 across all algorithms.These empirical results corroborate DBPSO's proficiency in delivering precise solutions for complex,high-dimensional optimisation challenges.
基金funded by Natural Science Foundation of Hubei Province Grant Numbers 2023AFB003,2023AFB004Education Department Scientific Research Program Project of Hubei Province of China Grant Number Q20222208+2 种基金Natural Science Foundation of Hubei Province of China(No.2022CFB076)Artificial Intelligence Innovation Project of Wuhan Science and Technology Bureau(No.2023010402040016)JSPS KAKENHI Grant Number JP22K12185.
文摘Complex optimization problems hold broad significance across numerous fields and applications.However,as the dimensionality of such problems increases,issues like the curse of dimensionality and local optima trapping also arise.To address these challenges,this paper proposes a novel Wild Gibbon Optimization Algorithm(WGOA)based on an analysis of wild gibbon population behavior.WGOAcomprises two strategies:community search and community competition.The community search strategy facilitates information exchange between two gibbon families,generating multiple candidate solutions to enhance algorithm diversity.Meanwhile,the community competition strategy reselects leaders for the population after each iteration,thus enhancing algorithm precision.To assess the algorithm’s performance,CEC2017 and CEC2022 are chosen as test functions.In the CEC2017 test suite,WGOA secures first place in 10 functions.In the CEC2022 benchmark functions,WGOA obtained the first rank in 5 functions.The ultimate experimental findings demonstrate that theWildGibbonOptimization Algorithm outperforms others in tested functions.This underscores the strong robustness and stability of the gibbonalgorithm in tackling complex single-objective optimization problems.
文摘Purpose–The purpose of this paper is to propose a fault-tolerant technology for increasing the durability of application programs when evolutionary computation is performed by fast parallel processing on many-core processors such as graphics processing units(GPUs)and multi-core processors(MCPs).Design/methodology/approach–For distributed genetic algorithm(GA)models,the paper proposes a method where an island’s ID number is added to the header of data transferred by this island for use in fault detection.Findings–The paper has shown that the processing time of the proposed idea is practically negligible in applications and also shown that an optimal solution can be obtained even with a single stuck-at fault or a transient fault,and that increasing the number of parallel threads makes the system less susceptible to faults.Originality/value–The study described in this paper is a new approach to increase the sustainability of application program using distributed GA on GPUs and MCPs.
