文献类型：期刊文献

英文题名：A novel improved grey Wolf optimization algorithm for numerical optimization and PID controller design

作者：Zhang, Tao[1]; Wang, Xin[2]; Wang, Zhenlei[1]

机构：[1] Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, 200237, China; [2] Center of Electrical Electronic Technology, Shanghai Jiao Tong University, Shanghai, 200240, China

年份：2018

起止页码：879

外文期刊名：Proceedings of 2018 IEEE 7th Data Driven Control and Learning Systems Conference, DDCLS 2018

收录：EI(收录号：20184806139119)

语种：英文

外文关键词：Proportional control systems - Biomimetics - Controllers - Electric control equipment - Three term control systems

摘要：The grey Wolf optimization (GWO) algorithm, one of the recently proposed bio-inspired algorithms, simulates the leadership hierarchy and hunting mechanism of grey wolves in nature. The GWO has a good performance in some optimization tasks, but its search capacity decreases with the increasing search scope and dimension. This paper proposes an improved GWO (IGWO) algorithm, in which Levy flight strategy and a sine cosine operator with adaptive step are incorporated to significantly improve the performance of the algorithm. The Levy flight strategy is used to strengthen the efficiency of global search. The adaptive sine cosine operator is introduced to improve the local search ability. Experimental results based on twenty unconstrained benchmark problems show the superiority of the proposed IGWO. Furthermore, the IGWO is utilized in PID controller design. The comparison results show that the IGWO algorithm is better than, or at least comparable to, other well-established swarm intelligence algorithms. ? 2018 IEEE.