Abstract
Combinatorial optimisation is often needed for solving real-world problems, which are often NP-hard so exact methods are not suitable. Instead local search methods are often effective to find near-optimal solutions quickly. However, it is difficult to determine which local search with what parameter setting should be optimal for a given problem. In this study two complex combinatorial optimisation are used, Multi-capacity Bin Packing Problems (MCBPP) and Google Machine Reassignment Problem (GMRP). Our experiments show that no single local search method could consistently achieve the best. They are sensitive to problem search space and parameters. Therefore we propose a hyper heuristic based method, which automatically selects the most appropriate local search during the search and tune the parameters accordingly. The results show that our proposed hyper-heuristic approach is effective and can achieve the overall best on multiple instances of both MCBPP and GMRP.
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Turky, A., Sabar, N.R., Dunstall, S., Song, A. (2018). Hyper-heuristic Based Local Search for Combinatorial Optimisation Problems. In: Mitrovic, T., Xue, B., Li, X. (eds) AI 2018: Advances in Artificial Intelligence. AI 2018. Lecture Notes in Computer Science(), vol 11320. Springer, Cham. https://doi.org/10.1007/978-3-030-03991-2_30
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DOI: https://doi.org/10.1007/978-3-030-03991-2_30
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