吉林大学学报（理学版）2025，Vol.63Issue(3)：845-854,10.DOI:10.13413/j.cnki.jdxblxb.2024005
采用动态种群策略的多目标粒子群优化算法
Multi-objective Particle Swarm Optimization Algorithm Using Dynamic Population Strategy
摘要
Abstract
Aiming at the problem that it was difficult to balance the diversity and convergence of multi-objective particle swarm optimization algorithms,we proposed a dynamic population-based multi-objective particle swarm optimization algorithm.The increase or decrease of the population size of this algorithm depended on the resources in the archive,thereby regulating the population size.On the one hand,particles were added by local perturbation based on grid technology to increase the local search ability of particles and improve the diversity of the algorithm.On the other hand,in order to prevent the population size from overgrowing,non-dominated ordering and population density were used to control the population size and accelerate the algorithm search progress,avoiding premature convergence.Five comparative algorithms were selected for experiments on test functions,and the experimental results show that this algorithm has obvious diversity and convergence advantages.关键词
动态种群/粒子群优化/多目标优化/多样性/收敛性Key words
dynamic population/particle swarm optimization/multi-objective optimization/diversity/convergence分类
信息技术与安全科学引用本文复制引用
杜睿山,井远光,付晓飞,孟令东,张豪鹏,王紫珊..采用动态种群策略的多目标粒子群优化算法[J].吉林大学学报（理学版）,2025,63(3):845-854,10.基金项目
国家重点研发计划项目(批准号:2022YFE0206800). （批准号:2022YFE0206800）
