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

Author

Listed:
  • Aymeric Blot

    (Université de Lille)

  • Marie-Éléonore Kessaci

    (Université de Lille)

  • Laetitia Jourdan

    (Université de Lille)

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.

Suggested Citation

  • Aymeric Blot & Marie-Éléonore Kessaci & Laetitia Jourdan, 2018. "Survey and unification of local search techniques in metaheuristics for multi-objective combinatorial optimisation," Journal of Heuristics, Springer, vol. 24(6), pages 853-877, December.
    • Handle: RePEc:spr:joheur:v:24:y:2018:i:6:d:10.1007_s10732-018-9381-1
    DOI: 10.1007/s10732-018-9381-1
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    References listed on IDEAS

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