Section: New Results

Indicator-based Multi-objective Local Search

Participant : A. Liefooghe.

In the last few years, a significant number of multi-objective metaheuristics have been proposed in the litterature in order to address real-world problems. Local search methods play a major role in many of these metaheuristic procedures. We adapt a recent and popular indicator-based selection method in order to define a population-based multi-objective local search. The proposed algorithm is designed in order to be easily adaptable, parameter independent and to have a high convergence rate. The capacity of our algorithm to reach these goals is evaluated on a large bunch of experiments. Three combinatorial optimization problems are investigated: a flow-shop scheduling problem, a ring star problem and a nurse scheduling problem. The experiments show that our algorithm can be applied with success to different types of multi-objective optimization problems and that it outperforms some classical metaheuristics. Furthermore, the parameter sensitivity analysis enables us to provide some useful guidelines about how to set the main parameters.