Decomposition of a Multiobjective Optimization Problem Into a Number of Simple Multiobjective Subproblems

• 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
• DOI: 10.1109/TEVC.2013.2281533

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.