吉林大学学报（理学版）Issue(6)：1223-1228,6.DOI:10.13413/j.cnki.jdxblxb.2015.06.29
基于混沌理论和自适应惯性权重的 PSO 算法优化
Optimization of PSO Algorithm Based on Chaotic Theory and Adaptive Inertia Weight
摘要
Abstract
In view of both fixed inertia weight and premature convergence obvious flaws of particle swarm optimization (PSO)algorithm,a dynamic adaptive adjustment strategy for inertia weight was proposed on the basis of a detailed analysis of the relationship among the inertia weight,population size,particle fitness and search space dimension,which effectively enhances the global and local optimization abilities of the algorithm.For the problem of premature,the chaotic mapping method was used to increase the diversity of the population,while the group extreme was adjusted in the direction of negative gradient,which greatly reduces the probability of fall into the local extreme.The correctness and effectiveness of the proposed PSO algorithm were verified to improve by some common used test functions compared with those by other algorithms.关键词
粒子群优化算法/混沌/惯性权重/自适应Key words
particle swarm optimization (PSO)algorithm/chaotic/inertia weight/adaptive分类
信息技术与安全科学引用本文复制引用
安鹏..基于混沌理论和自适应惯性权重的 PSO 算法优化[J].吉林大学学报（理学版）,2015,(6):1223-1228,6.基金项目
国家自然科学基金(批准号:61502256)和浙江省自然科学基金(批准号:LY15F020011) （批准号:61502256）
