软件导刊2023，Vol.22Issue(12)：132-138,7.DOI:10.11907/rjdk.222375
基于最优交叉的广泛学习粒子群优化
Comprehensive Learning Particle Swarm Optimization Based on Optimal Crossover
摘要
Abstract
Particle swarm optimization(PSO)algorithm has been widely used in large-scale complex problems such as resource allocation in recent years because of its simple implementation and easy operation.However,the slow convergence speed and low solution accuracy of the algorithm also restrict its further application.In order to solve the above problems,this paper introduces the chromosome crossing characteris-tics of genetic algorithm,and combines with comprehensive learning particle swarm optimization,proposes a comprehensive learning particle swarm optimization based on optimal crossing.It can improve the convergence speed of the algorithm and the accuracy of solving the problem by performing the optimal crossover operation between the global optimal particle position and the historical optimal position of the individual to obtain a better individual.The experimental results of benchmark function show that the proposed algorithm has faster convergence speed and optimization accuracy than the original algorithm,and the results of Friedman test and Wilcoxon signed-rank test show that the proposed algorithm has better advantages than other comparison algorithms.关键词
粒子群优化/遗传算法/广泛学习策略/最优交叉/Friedman检验/Wilcoxon符号秩检验Key words
particle swarm optimization/genetic algorithm/comprehensive learning strategy/optimal crossover/Friedman test/Wilcoxon signed-rank test分类
信息技术与安全科学引用本文复制引用
陈小斌,杨利华,汤可宗..基于最优交叉的广泛学习粒子群优化[J].软件导刊,2023,22(12):132-138,7.基金项目
江西省教育厅科学技术研究项目(GJJ210731,GJJ211331) （GJJ210731,GJJ211331）
