文献类型：期刊文献

英文题名：Oscillatory Particle Swarm Optimizer

作者：Shi, Haiyan[1];Liu, Shilong[2];Wu, Hongkun[2];Li, Ruowei[2];Liu, Sanchi[2];Kwok, Ngaiming[2];Peng, Yeping[3]

机构：[1]Shaoxing Univ, Sch Mech & Elect Engn, Shaoxing 312000, Zhejiang, Peoples R China;[2]Univ New South Wales, Sch Mech & Mfg Engn, Sydney, NSW 2052, Australia;[3]Shenzhen Univ, Coll Mechatron & Control Engn, Shenzhen Key Lab Electromagnet Control, Shenzhen 518060, Guangdong, Peoples R China

年份：2018

卷号：73

起止页码：316

外文期刊名：APPLIED SOFT COMPUTING

收录：SCI-EXPANDED(收录号：WOS:000450124900023)、、EI(收录号：20183805826431)、Scopus(收录号：2-s2.0-85053185916)、WOS

基金：This work is funded by the Natural Science Foundation of China, China (Grant Nos. 61603258, 51675403), Natural Science Foundation of Guangdong, China (Grant No. 2018A030310522), Shenzhen Science and Technology Planning Project, China (Grant No. JCYJ20170818100522101), and Natural Science Foundation of Shenzhen University, China (Grant No. 2017032).

语种：英文

外文关键词：Particle swarm optimizer; Oscillatory trajectory; Parameter setting

外文摘要：The Particle Swarm Optimization (PSO) algorithm is an attractive meta-heuristic approach for difficult optimization problems. It is able to produce satisfactory results when classical analytic methods cannot be applied. However, the design of PSO was usually based on ad-hoc attempts and its behavior could not be exactly specified. In this work, we propose to drive particle into oscillatory trajectories such that the search space can be covered more completely. A difference equation based analysis is conducted to reveal conditions that guarantee trajectory oscillation and solution convergence. The settings of cognitive and social learning factors and the inertia weight are then determined. In addition, a new strategy in directing these parameters to follow a linearly decreasing profile with a perturbation is formulated. Experiments on function optimizations are conducted and compared to currently available methods. Results have confirmed that the proposed Oscillatory Particle Swarm Optimizer (OSC-PSO) outperforms other recent PSO algorithms using adaptive inertia weights. (C)2018 Elsevier B.V. All rights reserved.