A Competitive Co-Evolutionary Approach for the Multi-Objective Evolutionary Algorithms
In multi-objective evolutionary algorithms (MOEAs), convergence and diversity are two basic issues and keeping a balance between them plays a vital role. There are several studies that have attempted to address this problem, but this is still an open challenge. It is thus the purpose of this researc...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9043554/ |
| _version_ | 1831678770484871168 |
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| author | Van Truong Vu Lam Thu Bui Trung Thanh Nguyen |
| author_facet | Van Truong Vu Lam Thu Bui Trung Thanh Nguyen |
| author_sort | Van Truong Vu |
| collection | DOAJ |
| description | In multi-objective evolutionary algorithms (MOEAs), convergence and diversity are two basic issues and keeping a balance between them plays a vital role. There are several studies that have attempted to address this problem, but this is still an open challenge. It is thus the purpose of this research to develop a dual-population competitive co-evolutionary approach to improving the balance between convergence and diversity. We utilize two populations to solve separate tasks. The first population uses Pareto-based ranking scheme to achieve better convergence, and the second one tries to guarantee population diversity via the use of a decomposition-based method. Next, by operating a competitive mechanism to combine the two populations, we create a new one with a view to having both characteristics (i.e. convergence and diversity). The proposed method's performance is measured by the renowned benchmarks of multi-objective optimization problems (MOPs) using the hypervolume (HV) and the inverted generational distance (IGD) metrics. Experimental results show that the proposed method outperforms cutting-edge co-evolutionary algorithms with a robust performance. |
| first_indexed | 2024-12-20T05:06:08Z |
| format | Article |
| id | doaj.art-95a809cc7f604910b95febf431d1d9eb |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T05:06:08Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-95a809cc7f604910b95febf431d1d9eb2022-12-21T19:52:24ZengIEEEIEEE Access2169-35362020-01-018569275694710.1109/ACCESS.2020.29822519043554A Competitive Co-Evolutionary Approach for the Multi-Objective Evolutionary AlgorithmsVan Truong Vu0Lam Thu Bui1Trung Thanh Nguyen2https://orcid.org/0000-0002-3268-1790Le Quy Don Technical University, Hanoi, VietnamLe Quy Don Technical University, Hanoi, VietnamLiverpool John Moores University, Liverpool, U.KIn multi-objective evolutionary algorithms (MOEAs), convergence and diversity are two basic issues and keeping a balance between them plays a vital role. There are several studies that have attempted to address this problem, but this is still an open challenge. It is thus the purpose of this research to develop a dual-population competitive co-evolutionary approach to improving the balance between convergence and diversity. We utilize two populations to solve separate tasks. The first population uses Pareto-based ranking scheme to achieve better convergence, and the second one tries to guarantee population diversity via the use of a decomposition-based method. Next, by operating a competitive mechanism to combine the two populations, we create a new one with a view to having both characteristics (i.e. convergence and diversity). The proposed method's performance is measured by the renowned benchmarks of multi-objective optimization problems (MOPs) using the hypervolume (HV) and the inverted generational distance (IGD) metrics. Experimental results show that the proposed method outperforms cutting-edge co-evolutionary algorithms with a robust performance.https://ieeexplore.ieee.org/document/9043554/Dual-populationconvergencediversityco-evolutioncompetitive |
| spellingShingle | Van Truong Vu Lam Thu Bui Trung Thanh Nguyen A Competitive Co-Evolutionary Approach for the Multi-Objective Evolutionary Algorithms IEEE Access Dual-population convergence diversity co-evolution competitive |
| title | A Competitive Co-Evolutionary Approach for the Multi-Objective Evolutionary Algorithms |
| title_full | A Competitive Co-Evolutionary Approach for the Multi-Objective Evolutionary Algorithms |
| title_fullStr | A Competitive Co-Evolutionary Approach for the Multi-Objective Evolutionary Algorithms |
| title_full_unstemmed | A Competitive Co-Evolutionary Approach for the Multi-Objective Evolutionary Algorithms |
| title_short | A Competitive Co-Evolutionary Approach for the Multi-Objective Evolutionary Algorithms |
| title_sort | competitive co evolutionary approach for the multi objective evolutionary algorithms |
| topic | Dual-population convergence diversity co-evolution competitive |
| url | https://ieeexplore.ieee.org/document/9043554/ |
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