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

Survey and unification of local search techniques in metaheuristics for multi-objective... 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 aymeric.blot@univ-lille.fr Marie-Éléonore Kessaci mkessaci@univ-lille.fr Laetitia Jourdan laetitia.jourdan@univ-lille.fr CNRS, http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Heuristics Springer Journals

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

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Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer Science+Business Media, LLC, part of Springer Nature
Subject
Mathematics; Operations Research, Management Science; Operations Research/Decision Theory; Artificial Intelligence (incl. Robotics); Calculus of Variations and Optimal Control; Optimization
ISSN
1381-1231
eISSN
1572-9397
D.O.I.
10.1007/s10732-018-9381-1
Publisher site
See Article on Publisher Site

Abstract

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 aymeric.blot@univ-lille.fr Marie-Éléonore Kessaci mkessaci@univ-lille.fr Laetitia Jourdan laetitia.jourdan@univ-lille.fr CNRS,

Journal

Journal of HeuristicsSpringer Journals

Published: May 29, 2018

References

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