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Conference Papers Year : 2013

On set-based local search for multiobjective combinatorial optimization

Abstract

In this paper, we formalize a multiobjective local search paradigm by combining set-based multiobjective optimization and neighborhood-based search principles. Approximating the Pareto set of a multiobjective optimization problem has been recently defined as a set problem, in which the search space is made of all feasible solution-sets. We here introduce a general set-based local search algorithm, explicitly based on a set-domain search space, evaluation function, and neighborhood relation. Different classes of set-domain neighborhood structures are proposed, each one leading to a different set-based local search variant. The corresponding methodology generalizes and unifies a large number of existing approaches for multiobjective optimization. Preliminary experiments on multiobjective NK-landscapes with objective correlation validates the ability of the set-based local search principles. Moreover, our investigations shed the light to further research on the efficient exploration of large-size set-domain neighborhood structures.
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Dates and versions

hal-00805166 , version 1 (02-03-2023)

Identifiers

  • HAL Id : hal-00805166 , version 1

Cite

Matthieu Basseur, Adrien Goëffon, Arnaud Liefooghe, Sébastien Verel. On set-based local search for multiobjective combinatorial optimization. GECCO 2013 - Genetic and Evolutionary Computation Conference, Jun 2013, Amsterdam, Netherlands. pp.471-478. ⟨hal-00805166⟩
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