摘要
针对粒子群优化算法中存在的局部收敛问题,提出一种融合惯性权重调整和群体最佳位置变异两种策略的粒子群优化算法.该算法将个体粒子的状态信息引入惯性权重策略,独立调整每个粒子的惯性权值,体现个体粒子对权重需求的差异.在最佳位置变异策略中采用分级思想,根据粒子群的搜索状态选择相应的极值变异方式,使变异操作更具针对性.实验结果表明,该算法对多个测试函数都表现出良好的优化性能,能有效避免局部收敛问题,提高了粒子群的全局搜索能力.
Aiming at the local convergence problem of particle swarm optimization algorithm,we proposed a particle swarm optimization algorithm based on the inertia weight adjustment and group best position variation.In this algorithm,the state information of individual particles was introduced into the inertia weight strategy.The inertia weight of each particle was adjusted independently,which reflected the difference of individual particles to the weight demand.In the mutation strategy of the best position,the classification idea was used.According to the searching state of the particle swarm,the corresponding extreme mutation mode was selected,which made the mutation operation more targeted.The experimental results indicate that the new algorithm shows good optimization performance for several test functions,which can effectively avoid local convergence problem and improve the global search ability of the particle swarm.
作者
刘振
周先存
LIU Zhen ZHOU Xiancun(School of Information Engineering, West Anhui University, Lu＇ an 237012, Anhui Province, China)
出处
《吉林大学学报（理学版）》
CAS
CSCD
北大核心
2017年第2期333-339,共7页
Journal of Jilin University:Science Edition
基金
国家自然科学基金(批准号:61303209
61572366)
关键词
独立惯性权重
分级变异
粒子群
优化算法
independent inertia weight
classification mutation
particle swarm
optimization algorithm
