Automatically Configuring Multi-objective Local Search Using Multi-objective Optimisation

Aymeric Blot 1, 2, 3, 4 Alexis Pernet 1, 2, 3, 4 Laetitia Jourdan 1, 2, 3, 4 Marie-Éléonore Kessaci-Marmion 1, 2, 3, 4 Holger Hoos 5, 6
1 ORKAD - Operational Research, Knowledge And Data
CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
2 DOLPHIN - Parallel Cooperative Multi-criteria Optimization
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
Abstract : Automatic algorithm configuration (AAC) is becoming an increasingly crucial component in the design of high-performance solvers for many challenging combinatorial optimisation problems. This raises the question how to most effectively leverage AAC in the context of building or optimising multi-objective optimisation algorithms, and specifically , multi-objective local search procedures. Because the performance of multi-objective optimisation algorithms cannot be fully characterised by a single performance indicator, we believe that AAC for multi-objective local search should make use of multi-objective configuration procedures. We test this belief by using MO-ParamILS to automatically configure a highly parametric iterated local search framework for the classical and widely studied bi-objective permutation flowshop problem. To the best of our knowledge, this is the first time a multi-objective optimisation algorithm is automatically configured in a multi-objective fashion, and our results demonstrate that this approach can produce very good results as well as interesting insights into the efficacy of various strategies and components of a flexible multi-objective local search framework.
Type de document :
Communication dans un congrès
Heike Trautmann; Günter Rudolph; Kathrin Klamroth; Oliver Schütze; Margaret Wiecek; Yaochu Jin; Christian Grimme. EMO 2017 - 9th International Conference on Evolutionary Multi-Criterion Optimization, Mar 2017, Münster, Germany. Springer, Lecture Notes in Computer Science, 10173, pp.61-73, 2017, Evolutionary Multi-Criterion Optimization (EMO 2017). 〈http://www.emo2017.org/〉. 〈10.1007/978-3-319-54157-0_5〉
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Contributeur : Aymeric Blot <>
Soumis le : lundi 10 juillet 2017 - 19:20:13
Dernière modification le : vendredi 19 janvier 2018 - 13:07:11
Document(s) archivé(s) le : mercredi 24 janvier 2018 - 17:33:06

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Aymeric Blot, Alexis Pernet, Laetitia Jourdan, Marie-Éléonore Kessaci-Marmion, Holger Hoos. Automatically Configuring Multi-objective Local Search Using Multi-objective Optimisation. Heike Trautmann; Günter Rudolph; Kathrin Klamroth; Oliver Schütze; Margaret Wiecek; Yaochu Jin; Christian Grimme. EMO 2017 - 9th International Conference on Evolutionary Multi-Criterion Optimization, Mar 2017, Münster, Germany. Springer, Lecture Notes in Computer Science, 10173, pp.61-73, 2017, Evolutionary Multi-Criterion Optimization (EMO 2017). 〈http://www.emo2017.org/〉. 〈10.1007/978-3-319-54157-0_5〉. 〈hal-01559690〉

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