Set-based discrete particle swarm optimization and its applications： a survey

作     者：Wei-Neng CHEN Da-Zhao TAN 

作者机构：School of Computer Science and Engineering South China University of Technology Guangzhou 510006 China 

出 版 物：《Frontiers of Computer Science》 (中国计算机科学前沿（英文版）)

年 卷 期：2018年第12卷第2期

页      面：203-216页

核心收录：

学科分类：080706[工学-化工过程机械] 12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0807[工学-动力工程及工程热物理] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Acknowledgements This work was supported in part by the National Natural Science Foundation of China (Grant Nos. 61622206 and 61379061)  in part by the Natural Science Foundation of Guangdong (2015A030306024)  in part by the Guangdong Special Support Program (2014TQ01X550)  and in part by the Guangzhou Pearl River New Star of Science and Technology (201506010002) 

主　　题：particle swarm optimization combinatorial optimization discrete optimization swarm intelligence setbased 

摘      要：Particle swarm optimization （PSO） is one of the most popular population-based stochastic algorithms for solving complex optimization problems. While PSO is simple and effective, it is originally defined in continuous space. In order to take advantage of PSO to solve combinatorial optimization problems in discrete space, the set-based PSO （S-PSO） framework extends PSO for discrete optimization by redefining the operations in PSO utilizing the set operations. Since its proposal, S-PSO has attracted increasing research attention and has become a promising approach for discrete optimization problems. In this paper, we intend to provide a comprehensive survey on the concepts, development and applications of S-PSO. First, the classification of discrete PSO algorithms is presented. Then the S-PSO framework is given. In particular, we will give an insight into the solution construction strategies, constraint handling strategies, and alternative reinforcement strategies in S-PSO together with its different variants. Furthermore, the extensions and applications of S-PSO are also discussed systemically. Some potential directions for the research of S-PSO are also discussed in this paper.