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International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 11/2019

11-02-2019 | Original Article

A decomposition based multiobjective evolutionary algorithm with self-adaptive mating restriction strategy

Authors: Xin Li, Hu Zhang, Shenmin Song

Published in: International Journal of Machine Learning and Cybernetics | Issue 11/2019

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Abstract

MOEA/D decomposes the multiobjective optimization problem into a number of subproblems. However, one subproblem’s requirement for exploitation and exploration varies with the evolutionary process. Furthermore, different subproblems’ requirements for exploitation and exploration are also different as the subproblems have been solved in distinct degree. This paper proposes a decomposition based multiobjective evolutionary algorithm with self-adaptive mating restriction strategy (MOEA/D-MRS). Considering the distinct solved degree of the subproblems, each subproblem has a separate mating restriction probability to control the contributions of exploitation and exploration. Besides, the mating restriction probability is updated by the survival length at each generation to adapt to the changing requirements. The experimental results validate that MOEA/D-MRS performs well on two test suites.
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Metadata
Title
A decomposition based multiobjective evolutionary algorithm with self-adaptive mating restriction strategy
Authors
Xin Li
Hu Zhang
Shenmin Song
Publication date
11-02-2019
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 11/2019
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-018-00919-w

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