The efficiency of indicator-based local search for multi-objective combinatorial optimisation problems |
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Authors: | M Basseur A Liefooghe K Le E K Burke |
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Institution: | (1) Department of Computer Science, University of Angers, 2, Boulevard Lavoisier, 49045 Angers Cedex 01, France |
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Abstract: | In the last few years, a significant number of multi-objective metaheuristics have been proposed in the literature in order
to address real-world problems. Local search methods play a major role in many of these metaheuristic procedures. In this
paper, we adapt a recent and popular indicator-based selection method proposed by Zitzler and Künzli in 2004, 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. In order to evaluate the capacity of our algorithm to reach these
goals, a large part of the paper is dedicated to experiments. Three combinatorial optimisation problems are tested: a flow
shop 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 optimisation 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 parameters. |
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