An Efficient Recursive Differential Grouping for Large-Scale Continuous Problems

Ming YANG, Aimin ZHOU, Changhe LI, Xin YAO

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

43 Citations (Scopus)

Abstract

Cooperative co-evolution (CC) is an efficient and practical evolutionary framework for solving large-scale optimization problems. The performance of CC is affected by the variable decomposition. An accurate variable decomposition can help to improve the performance of CC on solving an optimization problem. The variable grouping methods usually spend many computational resources obtaining an accurate variable decomposition. To reduce the computational cost on the decomposition, we propose an efficient recursive differential grouping (ERDG) method in this article. By exploiting the historical information on examining the interrelationship between the variables of an optimization problem, ERDG is able to avoid examining some interrelationship and spend much less computation than other recursive differential grouping methods. Our experimental results and analysis suggest that ERDG is a competitive method for decomposing large-scale continuous problems and improves the performance of CC for solving the large-scale optimization problems. © 1997-2012 IEEE.
Original languageEnglish
Article number9141328
Pages (from-to)159-171
Number of pages13
JournalIEEE Transactions on Evolutionary Computation
Volume25
Issue number1
Early online date15 Jul 2020
DOIs
Publication statusPublished - Feb 2021
Externally publishedYes

Bibliographical note

This work was supported in part by the Natural Science Foundation of Hubei Province under Grant 2019CFB584; in part by the National Natural Science Foundation of China under Grant 61305086 and Grant 61673355; and in part by the Fundamental Research Funds for the Central Universities, China University of Geosciences, Wuhan under Grant CUGL170412.

Keywords

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

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