Efficient Resource Allocation in Cooperative Co-Evolution for Large-Scale Global Optimization

Ming YANG, Mohammad Nabi OMIDVAR, Changhe LI, Xiaodong LI, Zhihua CAI, Borhan KAZIMIPOUR, Xin YAO

Research output: Journal PublicationsJournal Article (refereed)peer-review

97 Citations (Scopus)


Cooperative co-evolution (CC) is an explicit means of problem decomposition in multipopulation evolutionary algorithms for solving large-scale optimization problems. For CC, subpopulations representing subcomponents of a large-scale optimization problem co-evolve, and are likely to have different contributions to the improvement of the best overall solution to the problem. Hence, it makes sense that more computational resources should be allocated to the subpopulations with greater contributions. In this paper, we study how to allocate computational resources in this context and subsequently propose a new CC framework named CCFR to efficiently allocate computational resources among the subpopulations according to their dynamic contributions to the improvement of the objective value of the best overall solution. Our experimental results suggest that CCFR can make efficient use of computational resources and is a highly competitive CCFR for solving large-scale optimization problems. © 1997-2012 IEEE.
Original languageEnglish
Article number7784772
Pages (from-to)493-505
Number of pages13
JournalIEEE Transactions on Evolutionary Computation
Issue number4
Early online date17 Dec 2016
Publication statusPublished - Aug 2017
Externally publishedYes

Bibliographical note

The work was supported in part by the National Natural Science Foundation of China under Grant 61305086, Grant 61673355, Grant 61673354, Grant 61329302, and Grant 61305079, and in part by the EPSRC under Grant EP/K001523/1 and the Open Research Project of the Hubei Key Laboratory of Intelligent Geo-Information Processing under Grant KLIGIP201602.


  • Cooperative co-evolution (CC)
  • large-scale global optimization
  • problem decomposition
  • resource allocation


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