Abstract:
MOEA/D is a well-known optimization algorithm in dealing with complex multi-objective problems. It employs a simple differential evolution strategy to generate offspring ...Show MoreMetadata
Abstract:
MOEA/D is a well-known optimization algorithm in dealing with complex multi-objective problems. It employs a simple differential evolution strategy to generate offspring individuals. However, duo to the sensibility to the parameter setting in differential evolution strategy, MOEA/D performs poor in certain problems. To understand the influences of different DE strategies, this paper tries to investigate the overall performance of MOEA/D with different DE strategies. The experiment results demonstrate that DE/current-to-rand/1 strategy performs the best in all test problems.
Published in: 2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)
Date of Conference: 29-31 July 2017
Date Added to IEEE Xplore: 25 June 2018
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