基于自适应变异概率粒子群优化算法的研究

系统仿真学报 ›› 2016, Vol. 28 ›› Issue (4): 874-879.

基于自适应变异概率粒子群优化算法的研究

黄松¹, 田娜¹, 纪志成^1,2

1.江南大学物联网工程学院,无锡 214122;
2.轻工过程先进控制教育部重点实验室,无锡 214122

收稿日期:2014-11-14 修回日期:2015-03-10 出版日期:2016-04-08 发布日期:2020-07-02
作者简介:黄松(1984-),男,湖北随州,博士生,研究方向为智能控制;田娜(1983-),女,河北石家庄,博士后,研究方向为智能控制技术;纪志成(1959-),男,浙江宁波,教授,博导,研究方向为智能控制技术。
基金资助:
国家自然科学基金(61572238);国家高技术研究发展计划(2014 AA041505)

Study of Modified Particle Swarm Optimization Algorithm Based on Adaptive Mutation Probability

Huang Song¹, Tian Na¹, Ji Zhicheng^1,2

1. School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China;
2. Key Laboratory of Advanced Process Control for Light Industry, Ministry of Education, Wuxi 214122, China

Received:2014-11-14 Revised:2015-03-10 Online:2016-04-08 Published:2020-07-02

摘要/Abstract

摘要： 变异操作是解决粒子群算法早熟的一种有效方法。针对迭代过程中种群多样性变化的特点, 提出了一种自适应变异概率的混合变异粒子群优化算法。通过聚集度动态地调节每代粒子的变异概率, 并用这种变异概率对全局最优位置进行高斯和柯西混合变异和对最差个体最优位置进行自适应小波变异。通过在matlab中和其他几种变异的粒子群优化算法进行比较验证,结果证明该算法具有较高的收敛精度和较好的算法性能。

关键词: 粒子群算法, 变异概率, 自适应, 混合变异

Abstract: Mutation operator is an effective method to solve the premature of particle swarm optimization. According to the characteristic of population diversity, a modified particle swarm optimization based on adaptive mutation probability and hybrid mutation strategy was proposed. Aggregation degree was introduced to adjust the mutation probability of each generation, and a hybrid Gaussian and Cauchy mutation based on the global-best position and an adaptive wavelet mutation based on the worst personal-best position were carried out. The simulation of the comparisons with other particle swarm optimizations with mutation operator on matlab was proposed. The results demonstrate that the proposed algorithm can obtain higher accuracy solution and have better performance.

Key words: particle swarm optimization, mutation probability, adaptive, hybrid mutation

中图分类号:

TP18

黄松, 田娜, 纪志成. 基于自适应变异概率粒子群优化算法的研究[J]. 系统仿真学报, 2016, 28(4): 874-879.

Huang Song, Tian Na, Ji Zhicheng. Study of Modified Particle Swarm Optimization Algorithm Based on Adaptive Mutation Probability[J]. Journal of System Simulation, 2016, 28(4): 874-879.

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