On the Effect of Connectedness for Biobjective Multiple and Long Path Problems

Sébastien Verel (1, 2) , Arnaud Liefooghe (2, 3) , Jérémie Humeau (2, 4) , Laetitia Jourdan (2, 3) , Clarisse Dhaenens (2, 3)

1 Laboratoire d'Informatique, Signaux, et Systèmes de Sophia-Antipolis (I3S) / Groupe SCOBI
2 DOLPHIN - Parallel Cooperative Multi-criteria Optimization
3 LIFL - Laboratoire d'Informatique Fondamentale de Lille
4 CERI SN - IMT Nord Europe - Centre for Digital Systems

Recently, the property of connectedness has been claimed to give a strong motivation on the design of local search techniques for multiobjective combinatorial optimization (MOCO). Indeed, when connectedness holds, a basic Pareto local search, initialized with at least one non-dominated solution, allows to identify the efficient set exhaustively. However, this becomes quickly infeasible in practice as the number of efficient solutions typically grows exponentially with the instance size. As a consequence, we generally have to deal with a limited-size approximation, where a good sample set has to be found. In this paper, we propose the biobjective multiple and long path problems to show experimentally that, on the first problems, even if the efficient set is connected, a local search may be outperformed by a simple evolutionary algorithm in the sampling of the efficient set. At the opposite, on the second problems, a local search algorithm may successfully approximate a disconnected efficient set. Then, we argue that connectedness is not the single property to study for the design of local search heuristics for MOCO. This work opens new discussions on a proper definition of the multiobjective fitness landscape.

Domaines: Intelligence artificielle [cs.AI]

Sébastien Verel, Arnaud Liefooghe, Jérémie Humeau, Laetitia Jourdan, Clarisse Dhaenens. On the Effect of Connectedness for Biobjective Multiple and Long Path Problems. Learning and Intelligent OptimizatioN Conference (LION 5), Jan 2011, Rome, Italy. pp.31--45. ⟨hal-00550353⟩