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Conference Papers Year : 2015

On Maintaining Diversity in MOEA/D: Application to a Biobjective Combinatorial FJSP

José Juan
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Abstract

MOEA/D is a generic decomposition-based multiobjective optimization framework which has been proved to be extremely effective in solving a broad range of optimization problems especially for continuous domains. In this paper, we consider applying MOEA/D to solve a bi-objective scheduling combinatorial problem in which task durations and due-dates are uncertain. Surprisingly, we find that the conventional MOEA/D implementation provides poor performance in our application setting. We show that this is because the replacement strategy underlying MOEA/D is suffering some shortcomes that lead to low population diversity, and thus to premature convergence. Consequently, we investigate existing variants of MOEA/D and we propose a novel and simple alternative replacement component at the aim of maintaining population diversity. Through extensive experiments, we then provide a comprehensive analysis on the relative performance and the behavior of the considered algorithms. Besides being able to outperform existing MOEA/D variants, as well as the standard NSGA-II algorithm, our investigations provide new insights into the search ability of MOEA/D and highlight new research opportunities for improving its design components.
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Dates and versions

hal-01249130 , version 1 (30-12-2015)

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José Juan, Bilel Derbel. On Maintaining Diversity in MOEA/D: Application to a Biobjective Combinatorial FJSP. GECCO '15 - Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, Jul 2015, Madrid, Spain. pp.719-726 ⟨10.1145/2739480.2754774⟩. ⟨hal-01249130⟩
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