Efficient Resource Allocation in Cooperative Co-evolution for Large-scale Global Optimization

Research output: Contribution to journalArticlepeer-review

Authors

  • Ming Yang
  • Changhe Li
  • Xiaodong Li
  • Zhihua Cai
  • Borhan Kazimipour

Colleges, School and Institutes

External organisations

  • Southern University of Science and Technology, Shernzhen, China

Abstract

Cooperative Co-evolution (CC) is an explicit means of problem decomposition in multi-population 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 CC framework for solving large-scale optimization problems.

Details

Original languageEnglish
Pages (from-to)493-505
Number of pages13
JournalIEEE Transactions on Evolutionary Computation
Volume21
Issue number4
Early online date15 Dec 2016
Publication statusPublished - Aug 2017

Keywords

  • large-scale global optimization , Cooperative co-evolution , resource allocation , problem decomposition