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

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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.

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  1. CNRS, Centrale Lille, UMR 9189 CRIStAL, Cité Scientifique, Université de Lille, 59655, Villeneuve d’Ascq, France

    Aymeric Blot, Marie-Éléonore Kessaci & Laetitia Jourdan

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  1. Aymeric Blot
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  2. Marie-Éléonore Kessaci
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  3. Laetitia Jourdan
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Correspondence to Aymeric Blot.

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Blot, A., Kessaci, MÉ. & Jourdan, L. Survey and unification of local search techniques in metaheuristics for multi-objective combinatorial optimisation. J Heuristics 24, 853–877 (2018). https://doi.org/10.1007/s10732-018-9381-1

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