J Heuristics https://doi.org/10.1007/s10732-018-9381-1 Survey and uniﬁcation of local search techniques in metaheuristics for multi-objective combinatorial optimisation 1 1 Aymeric Blot · Marie-Éléonore Kessaci · Laetitia Jourdan Received: 20 December 2017 / Revised: 24 April 2018 / Accepted: 24 May 2018 © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Metaheuristics are algorithms that have proven their efﬁciency on multi- objective combinatorial optimisation problems. They often use local search tech- niques, 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 1 Introduction Metaheuristics are widely used algorithms for solving large and complex multi- objective optimisation problems (Gendreau and Potvin 2010). Indeed, most of such problems are NP-hard and require approximation mechanisms to obtain good solu- tions in a reasonable time. A common point of many multi-objective metaheuristics B Aymeric Blot email@example.com Marie-Éléonore Kessaci firstname.lastname@example.org Laetitia Jourdan email@example.com CNRS,
Journal of Heuristics – Springer Journals
Published: May 29, 2018
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