research-article

CCFR2: : A more efficient cooperative co-evolutionary framework for large-scale global optimization

Authors:

Xuesong YanAuthors Info & Claims

Volume 512, Issue C

Pages 64 - 79

https://doi.org/10.1016/j.ins.2019.09.065

Published: 01 February 2020 Publication History

Abstract

Cooperative co-evolution (CC) is an explicit means of divide-and-conquer strategy in evolutionary algorithms for solving large-scale optimization problems. The subcomponents generated by CC may have different characteristics. When optimizing the subcomponents, the settings of the subpopulations should take the characteristics into account. CCFR is a previously published CC framework which allocates computational resources among subpopulations according to the contributions of subpopulations to the improvement of the best overall objective value. In this paper, we propose an improved version of CCFR named CCFR2, which can specify unequal-sized subpopulations for optimizing different subcomponents of variables. CCFR2 computes the average improvement of the best overall objective value per fitness evaluation as the contribution of a subpopulation, which considers the subpopulation size in the contribution computation. A control parameter is adopted by CCFR2 to balance the effects of the historical and real-time improvements of the best overall objective value on the contribution computation. Compared with CCFR, CCFR2 is able to save computational resources from obtaining the best overall solution before the evolution starts and evaluating individuals in co-evolutionary cycles. Our experimental results and analysis suggest that CCFR2 improves the performance of CCFR and is a more efficient CC framework for solving large-scale optimization problems.

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CCFR2: A more efficient cooperative co-evolutionary framework for large-scale global optimization

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CCFR3: A cooperative co-evolution with efficient resource allocation for large-scale global optimization
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Published In

cover image Information Sciences: an International Journal

Information Sciences: an International Journal Volume 512, Issue C

Feb 2020

1606 pages

ISSN:0020-0255

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Elsevier Inc.

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Elsevier Science Inc.

United States

Publication History

Published: 01 February 2020

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Cited By

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https://dl.acm.org/doi/10.1145/3638529.3654171
Jiang PLiu JCheng Y(2024)Bi-Population-Enhanced Cooperative Differential Evolution for Constrained Large-Scale Optimization ProblemsIEEE Transactions on Evolutionary Computation10.1109/TEVC.2023.332500428:6(1620-1632)Online publication date: 1-Dec-2024
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Yang WLiu JTan SZhang WLiu Y(2024)Evolutionary dynamic grouping based cooperative co-evolution algorithm for large-scale optimizationApplied Intelligence10.1007/s10489-024-05390-554:6(4585-4601)Online publication date: 1-Mar-2024
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