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Geometric Differential Evolution in MOEA/D: A Preliminary Study

Saúl Zapotecas-Martínez 1 Bilel Derbel 2 Arnaud Liefooghe 2 Hernan Aguirre 1 Kiyoshi Tanaka 1 
2 DOLPHIN - Parallel Cooperative Multi-criteria Optimization
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189
Abstract : The multi-objective evolutionary algorithm based on decomposition (MOEA/D) is an aggregation-based algorithm which has became successful for solving multi-objective optimization problems (MOPs). So far, for the continuous domain, the most successful variants of MOEA/D are based on differential evolution (DE) operators. However, no investigations on the application of DE-like operators within MOEA/D exist in the context of combinatorial optimization. This is precisely the focus of the work reported in this paper. More particularly, we study the incorporation of geometric differential evolution (gDE), the discrete generalization of DE, into the MOEA/D framework. We conduct preliminary experiments in order to study the effectiveness of gDE when coupled with MOEA/D. Our results indicate that the proposed approach is highly competitive with respect to the original version of MOEA/D, when solving a combinatorial optimization problem having between two and four objective functions.
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Submitted on : Wednesday, December 30, 2015 - 11:42:48 AM
Last modification on : Tuesday, December 6, 2022 - 12:42:13 PM



Saúl Zapotecas-Martínez, Bilel Derbel, Arnaud Liefooghe, Hernan Aguirre, Kiyoshi Tanaka. Geometric Differential Evolution in MOEA/D: A Preliminary Study. The 14th LNCS-LNAI International Conference on Artificial Intelligence (MICAI), Oct 2015, Cuernavaca, Mexico. pp.364-376, ⟨10.1007/978-3-319-27060-9_30⟩. ⟨hal-01249127⟩



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