文献类型：期刊文献

英文题名：Multi-population techniques in nature inspired optimization algorithms: A comprehensive survey

作者：Ma, Haiping[1];Shen, Shigen[1];Yu, Mei[1];Yang, Zhile[2];Fei, Minrui[3];Zhou, Huiyu[4]

机构：[1]Shaoxing Univ, Dept Elect Engn, Shaoxing 312000, Zhejiang, Peoples R China;[2]Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen 518055, Peoples R China;[3]Shanghai Univ, Sch Mechatron Engn & Automat, Shanghai Key Lab Power Stn Automat Technol, Shanghai 200072, Peoples R China;[4]Univ Leicester, Dept Informat, Leicester LE1 7RH, Leics, England

年份：2019

卷号：44

起止页码：365

外文期刊名：SWARM AND EVOLUTIONARY COMPUTATION

收录：SCI-EXPANDED(收录号：WOS:000456761600026)、、EI(收录号：20181905144053)、Scopus(收录号：2-s2.0-85046356963)、WOS

基金：This material is based upon work supported by the National Natural Science Foundation of China under Grant Nos. 61640316, 61633016 and 61533010, Zhejiang Province Public Technology Applied Research Project under Grant No. 2017C31111. H. Zhou is supported by UK EPSRC under Grants EP/N508664/1, EP/R007187/1 and EP/N011074/1, and Royal Society-Newton Advanced Fellowship under Grant NA160342.

语种：英文

外文关键词：Multi-population; Nature-inspired algorithm; Optimization; Evolutionary algorithm; Swarm intelligence

外文摘要：Multi-population based nature-inspired optimization algorithms have attracted wide research interests in the last decade, and become one of the frequently used methods to handle real-world optimization problems. Considering the importance and value of multi-population methods and its applications, we believe it is the right time to provide a comprehensive survey of the published work, and also to discuss several aspects for the future research. The purpose of this paper is to summarize the published techniques related to the multi-population methods in nature-inspired optimization algorithms. Beginning with the concept of multi-population optimization, we review basic and important issues in the multi-population methods and discuss their applications in science and engineering. Finally, this paper presents several interesting open problems with future research directions for multi-population optimization methods.