Decomposition of a Multiobjective Optimization Problem Into a Number of Simple Multiobjective Subproblems
This letter suggests an approach for decomposing a multiobjective optimization problem (MOP) into a set of simple multiobjective optimization subproblems. Using this approach, it proposes MOEA/D-M2M, a new version of multiobjective optimization evolutionary algorithm-based decomposition. This propos...
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| Vydané v: | IEEE transactions on evolutionary computation Ročník 18; číslo 3; s. 450 - 455 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
New York, NY
IEEE
01.06.2014
Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Predmet: | |
| ISSN: | 1089-778X, 1941-0026 |
| On-line prístup: | Získať plný text |
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| Abstract | This letter suggests an approach for decomposing a multiobjective optimization problem (MOP) into a set of simple multiobjective optimization subproblems. Using this approach, it proposes MOEA/D-M2M, a new version of multiobjective optimization evolutionary algorithm-based decomposition. This proposed algorithm solves these subproblems in a collaborative way. Each subproblem has its own population and receives computational effort at each generation. In such a way, population diversity can be maintained, which is critical for solving some MOPs. Experimental studies have been conducted to compare MOEA/D-M2M with classic MOEA/D and NSGA-II. This letter argues that population diversity is more important than convergence in multiobjective evolutionary algorithms for dealing with some MOPs. It also explains why MOEA/D-M2M performs better. |
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| AbstractList | This letter suggests an approach for decomposing a multiobjective optimization problem (MOP) into a set of simple multiobjective optimization subproblems. Using this approach, it proposes MOEA/D-M2M, a new version of multiobjective optimization evolutionary algorithm-based decomposition. This proposed algorithm solves these subproblems in a collaborative way. Each subproblem has its own population and receives computational effort at each generation. In such a way, population diversity can be maintained, which is critical for solving some MOPs. Experimental studies have been conducted to compare MOEA/D-M2M with classic MOEA/D and NSGA-II. This letter argues that population diversity is more important than convergence in multiobjective evolutionary algorithms for dealing with some MOPs. It also explains why MOEA/D-M2M performs better. |
| Author | Zhang, Qingfu Gu, Fangqing Liu, Hai-Lin |
| Author_xml | – sequence: 1 givenname: Hai-Lin surname: Liu fullname: Liu, Hai-Lin email: hlliu@gdut.edu.cn organization: Guangdong University of Technology, Guangdong, China – sequence: 2 givenname: Fangqing surname: Gu fullname: Gu, Fangqing email: gufangqing84@163.com organization: Guangdong University of Technology, Guangdong, China – sequence: 3 givenname: Qingfu surname: Zhang fullname: Zhang, Qingfu email: qingfu.zhang@cityu.edu.hk organization: School of Computer Science and Electronic Engineering, University of Essex, Colchester, U.K |
| BackLink | http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=28562585$$DView record in Pascal Francis |
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| CODEN | ITEVF5 |
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| SubjectTerms | Algorithmics. Computability. Computer arithmetics Algorithms Applied sciences Approximation algorithms Approximation methods Computer science; control theory; systems Convergence Dealing Decision theory. Utility theory Decomposition Evolutionary Evolutionary algorithms Exact sciences and technology hybrid algorithms Linear programming Mathematical programming Mopping Multiobjective optimization Operational research and scientific management Operational research. Management science Optimization Radiocommunications Sociology Statistics Telecommunications Telecommunications and information theory Theoretical computing Vectors |
| Title | Decomposition of a Multiobjective Optimization Problem Into a Number of Simple Multiobjective Subproblems |
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