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

Liu, HL and Gu, F and Zhang, Q (2014) 'Decomposition of a multiobjective optimization problem into a number of simple multiobjective subproblems.' IEEE Transactions on Evolutionary Computation, 18 (3). 450 - 455. ISSN 1089-778X

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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.

Item Type: Article
Subjects: Q Science > QA Mathematics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Jim Jamieson
Date Deposited: 12 Nov 2014 20:42
Last Modified: 17 Oct 2019 14:17

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