A Review on Representative Swarm Intelligence Algorithms for Solving Optimization Problems:Applications and Trends  

作　　者：Jun Tang Gang Liu Qingtao Pan 

机构地区：[1]College of Systems Engineering,National University of Defense Technology,Changsha 410000,China [2]School of Information Science and Engineering,Hunan Institute of Science and Technology,Yueyang 414000,China

出　　处：《IEEE/CAA Journal of Automatica Sinica》2021年第10期1627-1643,共17页自动化学报（英文版）

基　　金：supported in part by the National Natural Science Foundation of China(62073330);in part by the Natural Science Foundation of Hunan Province(2019JJ20021,2020JJ4339);in part by the Scientific Research Fund of Hunan Province Education Department(20B272)。

摘　　要：Swarm intelligence algorithms are a subset of the artificial intelligence(AI)field,which is increasing popularity in resolving different optimization problems and has been widely utilized in various applications.In the past decades,numerous swarm intelligence algorithms have been developed,including ant colony optimization(ACO),particle swarm optimization(PSO),artificial fish swarm(AFS),bacterial foraging optimization(BFO),and artificial bee colony(ABC).This review tries to review the most representative swarm intelligence algorithms in chronological order by highlighting the functions and strengths from 127 research literatures.It provides an overview of the various swarm intelligence algorithms and their advanced developments,and briefly provides the description of their successful applications in optimization problems of engineering fields.Finally,opinions and perspectives on the trends and prospects in this relatively new research domain are represented to support future developments.

关 键 词：Ant colony optimization(ACO) artificial bee colony(ABC) artificial fish swarm(AFS) bacterial foraging optimization(BFO) optimization particle swarm optimization(PSO) swarm intelligence 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]