New Initialisation Techniques for Multi-objective Local Search

Aymeric Blot (1, 2) , Manuel López-Ibáñez (3) , Marie-Éléonore Kessaci (2, 1) , Laetitia Jourdan (1, 2)

1 ORKAD - Operational Research, Knowledge And Data
2 CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189
3 University of Manchester [Manchester]

Given the availability of high-performing local search (LS) for single-objective (SO) optimisation problems, one successful approach to tackle their multi-objective (MO) counterparts is scalarisation-based local search (SBLS). SBLS strategies solve multiple scalarisations, i.e., aggregations of the multiple objectives into a single scalar value, with varying weights. They have been shown to work specially well as the initialisation phase of other types of multi-objective local search, such as Pareto local search (PLS). A major drawback of existing SBLS strategies is that the underlying SO optimiser is unaware of the MO nature of the problem and only returns a single solution, discarding any intermediate solutions that may be of interest. We propose here two new SBLS strategies (ChangeRestart and Change-Direction) that overcome this drawback by augmenting the underlying SO-LS method with an archive of nondominated solutions that is used to dynamically update the scalarisations. The new strategies produce better results on the bi-objective permutation flowshop problem than other five SBLS strategies from the literature, not only on their own but also when used as the initialisation phase of PLS.

Mots clés: Flowshop scheduling; Local search; Heuristics; Multi-objective optimisation; Combinatorial optimisation

Domaines: Recherche opérationnelle [math.OC]

Aymeric Blot, Manuel López-Ibáñez, Marie-Éléonore Kessaci, Laetitia Jourdan. New Initialisation Techniques for Multi-objective Local Search: Application to the Bi-objective Permutation Flowshop. Parallel Problem Solving from Nature - PPSN XV, Sep 2018, Coimbra, Portugal. ⟨10.1007/978-3-319-99253-2_26⟩. ⟨hal-01868414⟩