Survey and unification of local search techniques in metaheuristics for multi-objective combinatorial optimisation

Aymeric Blot (), Marie-Éléonore Kessaci () and Laetitia Jourdan ()
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Aymeric Blot: Université de Lille
Marie-Éléonore Kessaci: Université de Lille
Laetitia Jourdan: Université de Lille

Journal of Heuristics, 2018, vol. 24, issue 6, No 2, 853-877

Abstract: Abstract Metaheuristics are algorithms that have proven their efficiency on multi-objective combinatorial optimisation problems. They often use local search techniques, either at their core or as intensification mechanisms, to obtain a well-converged and diversified final result. This paper surveys the use of local search techniques in multi-objective metaheuristics and proposes a general structure to describe and unify their underlying components. This structure can instantiate most of the multi-objective local search techniques and algorithms in literature.

Keywords: Multi-objective optimisation; Combinatorial optimisation; Metaheuristics; Unification; Local search algorithms (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10732-018-9381-1

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