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

Authors

Huang Song, 1. School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China;;
Tian Na, 1. School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China;;
Zhicheng Ji, 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;

Abstract

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.

Recommended Citation

Song, Huang; Na, Tian; and Ji, Zhicheng (2020) "Study of Modified Particle Swarm Optimization Algorithm Based on Adaptive Mutation Probability," Journal of System Simulation: Vol. 28: Iss. 4, Article 14.
Available at: https://dc-china-simulation.researchcommons.org/journal/vol28/iss4/14

First Page

874

Revised Date

2015-03-10

DOI Link

https://doi.org/

Last Page

879

CLC

TP18

Recommended Citation

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.