Boosting Cooperative Coevolution for Large Scale Optimization With a Fine-Grained Computation Resource Allocation Strategy

Cooperative coevolution (CC) has shown great potential for solving large-scale optimization problems (LSOPs). However, traditional CC algorithms often waste part of the computation resource (CR) as they equally allocate CR among all subproblems. The recently developed contribution-based CC algorithm...

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Bibliographic Details
Published in:IEEE transactions on cybernetics Vol. 49; no. 12; pp. 4180 - 4193
Main Authors: Ren, Zhigang, Liang, Yongsheng, Zhang, Aimin, Yang, Yang, Feng, Zuren, Wang, Lin
Format: Journal Article
Language:English
Published: United States IEEE 01.12.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Boosting
Business competition
Computation resource allocation (CRA)
cooperative coevolution (CC)
Cybernetics
differential evolution (DE)
Evolutionary computation
Fitness
Iterative methods
large scale optimization
Mathematical model
Mathematical models
Optimization
Partitioning algorithms
Resource allocation
Resource management
Sun
ISSN:2168-2267, 2168-2275, 2168-2275
Online Access:Get full text
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Summary:Cooperative coevolution (CC) has shown great potential for solving large-scale optimization problems (LSOPs). However, traditional CC algorithms often waste part of the computation resource (CR) as they equally allocate CR among all subproblems. The recently developed contribution-based CC algorithms improve the traditional ones to a certain extent by adaptively allocating CR according to some heuristic rules. Different from existing works, this paper explicitly constructs a mathematical model for the CR allocation (CRA) problem in CC and proposes a novel fine-grained CRA (FCRA) strategy by fully considering both the theoretically optimal solution of the CRA model and the evolution characteristics of CC. FCRA takes a single iteration as a basic CRA unit and always selects the subproblem which is most likely to make the largest contribution to the total fitness improvement to undergo a new iteration, where the contribution of a subproblem at a new iteration is estimated according to its current contribution, current evolution status, as well as the estimation for its current contribution. We verified the efficiency of FCRA by combining it with the success-history-based adaptive differential evolution which is an excellent DE variant but has never been employed in the CC framework. Experimental results on two benchmark suites for LSOPs demonstrate that FCRA significantly outperforms existing CRA strategies and the resulting CC algorithm is highly competitive in solving LSOPs.
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ISSN:2168-2267
2168-2275
2168-2275
DOI:10.1109/TCYB.2018.2859635