A two-phase framework of locating the reference point for decomposition-based constrained multi-objective evolutionary algorithms
Reference point is a key component in decomposition-based constrained multi-objective evolutionary algorithms (CMOEAs). A proper way of updating it requires considering constraint-handling techniques due to the existing constraints. However, it remains unexplored in this field. To remedy this issue,...
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| Vydáno v: | Knowledge-based systems Ročník 239; s. 107933 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Amsterdam
Elsevier B.V
05.03.2022
Elsevier Science Ltd |
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| ISSN: | 0950-7051, 1872-7409 |
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| Abstract | Reference point is a key component in decomposition-based constrained multi-objective evolutionary algorithms (CMOEAs). A proper way of updating it requires considering constraint-handling techniques due to the existing constraints. However, it remains unexplored in this field. To remedy this issue, this paper firstly designs a set of benchmark problems with difficulties that a CMOEA must update the reference point effectively. Then a two-phase framework of locating the reference point is proposed to enhance performance of the current decomposition-based CMOEAs by evolving two populations—the main and external population. At the first phase, the external population evolves along with the main population to identify the approximate locations of the constrained and unconstrained Pareto front (PF). At the second phase, a location estimation mechanism is designed to estimate the best fit reference point between the two PFs for the main population by evolving the external population. Besides, a replacement strategy is used to drive the main population to the promising regions. Experimental studies are conducted on 26 benchmark problems, and the results highlight the effectiveness of the proposed framework. |
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| AbstractList | Reference point is a key component in decomposition-based constrained multi-objective evolutionary algorithms (CMOEAs). A proper way of updating it requires considering constraint-handling techniques due to the existing constraints. However, it remains unexplored in this field. To remedy this issue, this paper firstly designs a set of benchmark problems with difficulties that a CMOEA must update the reference point effectively. Then a two-phase framework of locating the reference point is proposed to enhance performance of the current decomposition-based CMOEAs by evolving two populations-the main and external population. At the first phase, the external population evolves along with the main population to identify the approximate locations of the constrained and unconstrained Pareto front (PF). At the second phase, a location estimation mechanism is designed to estimate the best fit reference point between the two PFs for the main population by evolving the external population. Besides, a replacement strategy is used to drive the main population to the promising regions. Experimental studies are conducted on 26 benchmark problems, and the results highlight the effectiveness of the proposed framework. |
| ArticleNumber | 107933 |
| Author | Goodman, Erik D. Tan, Kay Chen Peng, Chaoda Liu, Hai-Lin |
| Author_xml | – sequence: 1 givenname: Chaoda surname: Peng fullname: Peng, Chaoda email: ChaodaPeng@scau.edu.cn organization: College of Mathematics and Informatics, South China Agricultural University, Guangzhou, Guangdong Province, China – sequence: 2 givenname: Hai-Lin orcidid: 0000-0003-2276-1938 surname: Liu fullname: Liu, Hai-Lin email: hlliu@gdut.edu.cn organization: School of Mathematics and Statistics, Guangdong University of Technology, Guangzhou city, Guangdong Province, China – sequence: 3 givenname: Erik D. orcidid: 0000-0002-2419-0692 surname: Goodman fullname: Goodman, Erik D. email: goodman@egr.msu.edu organization: BEACON Center for the Study of Evolution in Action, Michigan State University, East Lansing, MI, USA – sequence: 4 givenname: Kay Chen surname: Tan fullname: Tan, Kay Chen email: kctan@polyu.edu.hk organization: Department of Computing, The Hong Kong Polytechnic University, Hong Kong SAR |
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| Keywords | Decomposition Referent point Constraint-handling technique Multi-objective evolutionary algorithm |
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| Title | A two-phase framework of locating the reference point for decomposition-based constrained multi-objective evolutionary algorithms |
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