Multi-objective Local Search Based on Decomposition

Bilel Derbel (1) , Arnaud Liefooghe (2, 1) , Qingfu Zhang (3) , Hernan Aguirre (4) , Kiyoshi Tanaka (4)

1 DOLPHIN - Parallel Cooperative Multi-criteria Optimization
2 CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189
3 CUHK - City University of Hong Kong [Hong Kong]
4 Faculty of Engineering [Nagano]

It is generally believed that Local search (Ls) should be used as a basic tool in multi-objective evolutionary computation for combi-natorial optimization. However, not much effort has been made to investigate how to efficiently use Ls in multi-objective evolutionary computation algorithms. In this paper, we study some issues in the use of cooperative scalarizing local search approaches for decomposition-based multi-objective combinatorial optimization. We propose and study multiple move strategies in the Moea/d framework. By extensive experiments on a new set of bi-objective traveling salesman problems with tunable correlated objectives, we analyze these policies with different Moea/d parameters. Our empirical study has shed some insights about the impact of the Ls move strategy on the anytime performance of the algorithm.

Domaines: Recherche opérationnelle [math.OC]; Optimisation et contrôle [math.OC]

Bilel Derbel, Arnaud Liefooghe, Qingfu Zhang, Hernan Aguirre, Kiyoshi Tanaka. Multi-objective Local Search Based on Decomposition. International Conference on Parallel Problem Solving from Nature (PPSN 2016), 2016, Edinburgh, United Kingdom. pp.431 - 441, ⟨10.1007/978-3-319-45823-6_40⟩. ⟨hal-01380632⟩