ED-LS: a heuristic local search for the firefighter problem
AbstractThis abstract summarizes the results reported in the paper . In this paper a new method of performing the local search for the multiobjective Firefighter Problem (FFP) is proposed. The proposed method reduces the size of the neighbourhood in which the local search looks for improved solutions by using heuristics to decide which solutions in the neighbourhood should be visited. In the paper the proposed local search method is used for improving solutions produced by two commonly used evolutionary algorithms: the MOEA/D and the NSGA-II. In the experiments the proposed method outperformed both the evolutionary algorithms without any local search (the 'None' method) as well as the algorithms combined with the local search visiting all solutions in the neighbourhood (the 'Full' method). Comparison to the local search selecting the same number of solutions as the ED-LS, but at random (the 'Random' method) shows that the proposed heuristics are indeed selecting good solutions to visit. Presented research can be extended in further studies. One possibility is to study in-depth how to balance computational resources between the evolutionary and local search algorithms. Second possibility is to employ more advanced models instead of the heuristics used in the paper
|Publication size in sheets||0.3|
|Book||Takadama Keiki, Aguirre Hernan (eds.): Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'18), 2018, Association for Computing Machinery, ISBN 9781450356183, 1546 p.|
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