### Study on Particle Swarm Optimization algorithm with parameters adaptive mutation based on particle entropy

LI Huaijun1, XIE Xiaopeng2, XIAO Xinyuan1

1. 1.Vehicle Safety Engineering Technology Center, Guangdong Communication Polytechnic, Guangzhou 510650, China
2.Automobile Tribology and Fault Diagnosis Institute, South China University of Technology, Guangzhou 510640, China
• Online:2014-10-01 Published:2014-09-29

### 基于粒子熵的参数自适应变异PSO算法研究

1. 1.广东交通职业技术学院 车辆安全工程技术中心，广州 510650
2.华南理工大学 汽车摩擦学与故障诊断研究所，广州 510640

Abstract: A new Particle Swarm Optimization（PSO） algorithm with global optimization of parameters for adaptive mutation is proposed around two key parameters[w]and[pgd]which all affect PSO algorithm performance to avoid the possible problems about local convergence and low precision. The concept of particle entropy set is defined which can accurately reflect the PSO data global aggregation behavior. The regression variance for inertia weight[w]of swarm dimensional data and the random variance for global variable[pgd]are determined by the particle entropy of every dimension data, and the method of using mutation frequency factor is used to avoid divergence in the algorithm. Simulation results show that compared with the conventional algorithm there are great advantages in optimization precision, getting rid of local traps, stability, etc, and good performance in solving complex multimodal problems with this algorithm.