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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Bibliographic Details
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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Description
Summary: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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ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2013.2281533