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research-article

Cooperative coevolution for large-scale global optimization based on fuzzy decomposition

Authors:

Quan WangAuthors Info & Claims

Soft Computing, Volume 25, Issue 5

Pages 3593 - 3608

https://doi.org/10.1007/s00500-020-05389-3

Published: 01 March 2021 Publication History

Abstract

Cooperative coevolution (CC) is an effective evolutionary divide-and-conquer strategy that solves large-scale global optimization (LSGO) by decomposing the problem into a set of lower-dimensional subproblems. The main challenge of CC is to find an optimal decomposition. Differential Grouping (DG) is a competitive decomposition method to identify the variable interaction with several improved versions like GDG and DG2. Although DG-based decomposition methods have shown superior performance compared to the other decomposition methods, they still have difficulty to deal with the overlapping problems since their optimal decomposition is unknown. To address this issue, instead of pursuing the high accuracy of decomposition, we propose a novel fuzzy decomposition algorithm that groups the variables according to their interaction degree. In the proposed fuzzy decomposition algorithm, the interaction structure matrix and the interactive degree for a LSGO problem are calculated at first according to the interaction among all the decision variables. Then the number of subgroups is determined based on the interactive degree. Based on the interaction structure matrix, a spectral clustering algorithm is proposed to decompose the decision variables with regard to the number of subgroups in order to achieve a better balance between high grouping accuracy and suitable group size. The proposed decomposition algorithm with DECC has been proven to outperform several state-of-the-art algorithms on the latest LSGO benchmark functions.

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Cited By

Xia WHongwei GMingde ZYaqing HMingyang S(2024)Stochastic online decisioning hyper-heuristic for high dimensional optimizationApplied Intelligence10.1007/s10489-023-05185-054:1(544-564)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s10489-023-05185-0
Fonseca TNassar Sde Oliveira AAgard B(2023)Low-Dimensional Space Modeling-Based Differential Evolution for Large-Scale Global Optimization ProblemsIEEE Transactions on Evolutionary Computation10.1109/TEVC.2022.322744027:5(1529-1543)Online publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1109/TEVC.2022.3227440
Kelkawi AEl-Abd MAhmad I(2023)GPU-based cooperative coevolution for large-scale global optimizationNeural Computing and Applications10.1007/s00521-022-07931-w35:6(4621-4642)Online publication date: 1-Feb-2023
https://dl.acm.org/doi/10.1007/s00521-022-07931-w
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cover image Soft Computing - A Fusion of Foundations, Methodologies and Applications

Soft Computing - A Fusion of Foundations, Methodologies and Applications Volume 25, Issue 5

Mar 2021

796 pages

ISSN:1432-7643

EISSN:1433-7479

Issue’s Table of Contents

© Springer-Verlag GmbH Germany, part of Springer Nature 2020.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 March 2021

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National Key R&D Program of China
National Natural Science Foundation of China
Natural Science Foundation of China

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Cited By

Xia WHongwei GMingde ZYaqing HMingyang S(2024)Stochastic online decisioning hyper-heuristic for high dimensional optimizationApplied Intelligence10.1007/s10489-023-05185-054:1(544-564)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s10489-023-05185-0
Fonseca TNassar Sde Oliveira AAgard B(2023)Low-Dimensional Space Modeling-Based Differential Evolution for Large-Scale Global Optimization ProblemsIEEE Transactions on Evolutionary Computation10.1109/TEVC.2022.322744027:5(1529-1543)Online publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1109/TEVC.2022.3227440
Kelkawi AEl-Abd MAhmad I(2023)GPU-based cooperative coevolution for large-scale global optimizationNeural Computing and Applications10.1007/s00521-022-07931-w35:6(4621-4642)Online publication date: 1-Feb-2023
https://dl.acm.org/doi/10.1007/s00521-022-07931-w
Fonseca TNassar Sde Oliveira AAgard B(2023)Low-Dimensional Space Modeling-Based Differential Evolution: A Scalability Perspective on bbob-largescale suiteAdvances in Computational Intelligence10.1007/978-3-031-43085-5_2(16-28)Online publication date: 19-Jun-2023
https://dl.acm.org/doi/10.1007/978-3-031-43085-5_2
Omidvar MLi XYao X(2022)A Review of Population-Based Metaheuristics for Large-Scale Black-Box Global Optimization—Part IIEEE Transactions on Evolutionary Computation10.1109/TEVC.2021.313083826:5(802-822)Online publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1109/TEVC.2021.3130838
Omidvar MLi XYao X(2022)A Review of Population-Based Metaheuristics for Large-Scale Black-Box Global Optimization—Part IIIEEE Transactions on Evolutionary Computation10.1109/TEVC.2021.313083526:5(823-843)Online publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1109/TEVC.2021.3130835

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