Evolution Consistency Based Decomposition for Cooperative Coevolution
Cooperative coevolution has been proven a promising framework for large-scale optimization. However, its performance heavily relies on the problem decomposition strategy which decomposes a high dimensional problem into exclusive smaller sub-problems. Though many decomposition methods have been devel...
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| Format: | Article |
| Language: | English |
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IEEE
2018-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8458106/ |
| _version_ | 1818645119531745280 |
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| author | Qiang Yang Wei-Neng Chen Jun Zhang |
| author_facet | Qiang Yang Wei-Neng Chen Jun Zhang |
| author_sort | Qiang Yang |
| collection | DOAJ |
| description | Cooperative coevolution has been proven a promising framework for large-scale optimization. However, its performance heavily relies on the problem decomposition strategy which decomposes a high dimensional problem into exclusive smaller sub-problems. Though many decomposition methods have been developed in recent years, they are confronted with limitations in capturing the interdependency among variables and costing a large number of fitness evaluations in the decomposition stage. To alleviate these issues, this paper proposes a data-driven decomposition method, which is called affinity propagation assisted and evolution consistency based decomposition, for cooperative coevolution. Specifically, we take advantage of historical evolutionary data to mine the evolution consistency among variables. Then, based on the mined consistency, we leverage the affinity propagation clustering algorithm to adaptively separate variables into groups with each group as a sub-problem. Particularly, this decomposition method is a dynamic variable grouping strategy, which is executed periodically during the evolution. The most advantageous property of this method is that it does not cost any fitness evaluations in the decomposition stage and could self-adaptively divide variables into groups. Extensive comparison results on two widely used large-scale benchmark sets demonstrate that the proposed decomposition method could assist the cooperative coevolution algorithm to achieve competitive or even better optimization performance than state-of-the-art decomposition methods. Therefore, the proposed decomposition strategy provides a new way to decompose variables into groups. |
| first_indexed | 2024-12-17T00:25:41Z |
| format | Article |
| id | doaj.art-172521b8839d49a49e991335f15e37a2 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T00:25:41Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-172521b8839d49a49e991335f15e37a22022-12-21T22:10:28ZengIEEEIEEE Access2169-35362018-01-016510845109710.1109/ACCESS.2018.28693348458106Evolution Consistency Based Decomposition for Cooperative CoevolutionQiang Yang0Wei-Neng Chen1https://orcid.org/0000-0003-0843-5802Jun Zhang2School of Data and Computer Science, Sun Yat-sen University, Guangzhou, ChinaSchool of Computer Science and Engineering, South China University of Technology, Guangzhou, ChinaSchool of Computer Science and Engineering, South China University of Technology, Guangzhou, ChinaCooperative coevolution has been proven a promising framework for large-scale optimization. However, its performance heavily relies on the problem decomposition strategy which decomposes a high dimensional problem into exclusive smaller sub-problems. Though many decomposition methods have been developed in recent years, they are confronted with limitations in capturing the interdependency among variables and costing a large number of fitness evaluations in the decomposition stage. To alleviate these issues, this paper proposes a data-driven decomposition method, which is called affinity propagation assisted and evolution consistency based decomposition, for cooperative coevolution. Specifically, we take advantage of historical evolutionary data to mine the evolution consistency among variables. Then, based on the mined consistency, we leverage the affinity propagation clustering algorithm to adaptively separate variables into groups with each group as a sub-problem. Particularly, this decomposition method is a dynamic variable grouping strategy, which is executed periodically during the evolution. The most advantageous property of this method is that it does not cost any fitness evaluations in the decomposition stage and could self-adaptively divide variables into groups. Extensive comparison results on two widely used large-scale benchmark sets demonstrate that the proposed decomposition method could assist the cooperative coevolution algorithm to achieve competitive or even better optimization performance than state-of-the-art decomposition methods. Therefore, the proposed decomposition strategy provides a new way to decompose variables into groups.https://ieeexplore.ieee.org/document/8458106/Large scale optimizationhigh dimensional problemscooperative coevolutiondecompositionevolutionary algorithms |
| spellingShingle | Qiang Yang Wei-Neng Chen Jun Zhang Evolution Consistency Based Decomposition for Cooperative Coevolution IEEE Access Large scale optimization high dimensional problems cooperative coevolution decomposition evolutionary algorithms |
| title | Evolution Consistency Based Decomposition for Cooperative Coevolution |
| title_full | Evolution Consistency Based Decomposition for Cooperative Coevolution |
| title_fullStr | Evolution Consistency Based Decomposition for Cooperative Coevolution |
| title_full_unstemmed | Evolution Consistency Based Decomposition for Cooperative Coevolution |
| title_short | Evolution Consistency Based Decomposition for Cooperative Coevolution |
| title_sort | evolution consistency based decomposition for cooperative coevolution |
| topic | Large scale optimization high dimensional problems cooperative coevolution decomposition evolutionary algorithms |
| url | https://ieeexplore.ieee.org/document/8458106/ |
| work_keys_str_mv | AT qiangyang evolutionconsistencybaseddecompositionforcooperativecoevolution AT weinengchen evolutionconsistencybaseddecompositionforcooperativecoevolution AT junzhang evolutionconsistencybaseddecompositionforcooperativecoevolution |