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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Published in:	IEEE transactions on evolutionary computation Vol. 18; no. 3; pp. 450 - 455
Main Authors:	Liu, Hai-Lin, Gu, Fangqing, Zhang, Qingfu
Format:	Journal Article
Language:	English
Published:	New York, NY IEEE 01.06.2014 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	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 multiobjective optimization hybrid algorithms Decomposition Pervasive computing Genetic algorithm Evolutionary algorithm Multiobjective programming Set computation Experimental study Machine to machine Mathematical programming
ISSN:	1089-778X, 1941-0026
Online Access:	Get full 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.
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
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Snippet	This letter suggests an approach for decomposing a multiobjective optimization problem (MOP) into a set of simple multiobjective optimization subproblems....
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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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