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

Haiping Ma*, Shigen Shen, Mei Yu, Zhile Yang, Minrui Fei, Huiyu Zhou

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

57 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)365-387
Number of pages23
JournalSwarm and Evolutionary Computation
Volume44
Early online date25 Apr 2019
DOIs
Publication statusEarly online date - 25 Apr 2019

Bibliographical note

Funding Information:
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 .

Funding Information:
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.

Publisher Copyright:
© 2018 Elsevier B.V.

Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.

Keywords

  • Evolutionary algorithm
  • Multi-population
  • Nature-inspired algorithm
  • Optimization
  • Swarm intelligence

ASJC Scopus subject areas

  • Computer Science(all)
  • Mathematics(all)

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