Decomposition of a multiobjective optimization problem into a number of simple multiobjective subproblems

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

150 Scopus Citations
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Original languageEnglish
Article number6595549
Pages (from-to)450-455
Journal / PublicationIEEE Transactions on Evolutionary Computation
Volume18
Issue number3
StatePublished - Jun 2014

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. © 2013 IEEE.

Research Area(s)

  • decomposition, hybrid algorithms, Multiobjective optimization