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Stable Matching-Based Selection in Evolutionary Multiobjective Optimization

Ke Li, and Qingfu Zhang, and Sam Kwong, and Miqing Li, and Ran Wang, (2014) 'Stable Matching-Based Selection in Evolutionary Multiobjective Optimization.' IEEE Transactions on Evolutionary Computation, 18 (6). pp. 909-923. ISSN 1089-778X

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Abstract

Multiobjective evolutionary algorithm based on decomposition (MOEA/D) decomposes a multiobjective optimization problem into a set of scalar optimization subproblems and optimizes them in a collaborative manner. Subproblems and solutions are two sets of agents that naturally exist in MOEA/D. The selection of promising solutions for subproblems can be regarded as a matching between subproblems and solutions. Stable matching, proposed in economics, can effectively resolve conflicts of interests among selfish agents in the market. In this paper, we advocate the use of a simple and effective stable matching (STM) model to coordinate the selection process in MOEA/D. In this model, subproblem agents can express their preferences over the solution agents, and vice versa. The stable outcome produced by the STM model matches each subproblem with one single solution, and it tradeoffs convergence and diversity of the evolutionary search. Comprehensive experiments have shown the effectiveness and competitiveness of our MOEA/D algorithm with the STM model. We have also demonstrated that user-preference information can be readily used in our proposed algorithm to find a region that decision makers are interested in.

Item Type: Article
Subjects: Q Science > QA Mathematics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 12 Nov 2014 20:45
Last Modified: 15 Jan 2022 00:57
URI: http://repository.essex.ac.uk/id/eprint/11559

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