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Conference Papers Year : 2018

A set-oriented MOEA/D

Abstract

The working principles of the well-established multi-objective evolutionary algorithm MOEA/D relies on the iterative and cooperative improvement of a number of single-objective sub-problems obtained by decomposition. Besides the definition of sub-problems, selection and replacement are, like in any evolutionary algorithm, the two core elements of MOEA/D. We argue that these two components are however loosely coupled with the maintained population. Thereby, we propose to re-design the working principles of MOEA/D by adopting a set-oriented perspective, where a many-to-one mapping between sub-problems and solutions is considered. Selection is then performed by defining a neighborhood relation among solutions in the population set, depending on the corresponding sub-problem mapping. Replacement is performed following an elitist mechanism allowing the population to have a variable, but bounded, cardinality during the search process. By conducting a comprehensive empirical analysis on a range of combinatorial multi- and many-objective nk-landscapes, we show that the proposed approach leads to significant improvements, especially when dealing with an increasing number of objectives. Our findings indicate that a set-oriented design can constitute a sound alternative for strengthening the practice of multi- and many-objective evolutionary optimization based on decomposition.
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Dates and versions

hal-01823671 , version 1 (13-11-2018)

Identifiers

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Bilel Derbel, Arnaud Liefooghe, Qingfu Zhang, Sébastien Verel, Hernan Aguirre, et al.. A set-oriented MOEA/D. GECCO 2018 - Genetic and Evolutionary Computation Conference, Jul 2018, Kyoto, Japan. pp.617-624, ⟨10.1145/3205455.3205575⟩. ⟨hal-01823671⟩
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