Hybrid Particle Swarm Optimization with Differential Evolution for Numerical and Engineering Optimization

作     者：Guo-Han Lin Jing Zhang Zhao-Hua Liu 

作者机构：College of Electrical and Information Engineering Hunan University College of Electrical and Information Hunan Institute of Engineering School of Information and Electrical Engineering Hunan University of Science and Technology 

出 版 物：《International Journal of Automation and computing》 (国际自动化与计算杂志（英文版）)

年 卷 期：2018年第15卷第1期

页      面：103-114页

核心收录：

学科分类：08[工学] 081104[工学-模式识别与智能系统] 0802[工学-机械工程] 0811[工学-控制科学与工程] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 081202[工学-计算机软件与理论] 

基　　金：supported by National Natural Science Foundation of China(Nos.61174140 and 61203016) Ph.D.Programs Foundation of Ministry of Education of China(No.20110161110035) China Postdoctoral Science Foundation Funded Project(No.2013M540628) 

主　　题：Particle swarm optimization （PSO） active disturbance rejection control （ADRC） differential evolution algorithm chaoticmap parameter tuning. 

摘      要：In this paper, a hybrid particle swarm optimization （PSO） algorithm with differential evolution （DE） is proposed for numerical benchmark problems and optimization of active disturbance rejection controller （ADRC） parameters. A chaotic map with greater Lyapunov exponent is introduced into PSO for balancing the exploration and exploitation abilities of the proposed algorithm. A DE operator is used to help PSO jump out of stagnation. Twelve benchmark function tests from CEC2005 and eight real world opti- mization problems from CEC2011 are used to evaluate the performance of the proposed algorithm. The results show that statistically, the proposed hybrid algorithm has performed consistently well compared to other hybrid variants. Moreover, the simulation results on ADRC parameter optimization show that the optimized ADRC has better robustness and adaptability for nonlinear discrete-time systems with time delays.