Abstract
As many real-world optimization problems become increasingly complex and hard to solve, better optimization algorithms are always needed. Nature inspired algorithms such as genetic algorithms and simulated annealing which belongs to the class of evolutionary algorithms are regarded as highly successful algorithms when applied to a broad range of discrete as well continuous optimization problems. This paper introduces the multilevel paradigm combined with genetic algorithm and simulated annealing for solving the maximum constraint satisfaction problem. The promising performances achieved by the proposed approach is demonstrated by comparisons made to solve conventional random benchmark problems.
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Bouhmala, N., Groesland, M.S., Volden-Freberg, V. (2016). Enhanced Metaheuristics with the Multilevel Paradigm for MAX-CSPs. In: Gervasi, O., et al. Computational Science and Its Applications -- ICCSA 2016. ICCSA 2016. Lecture Notes in Computer Science(), vol 9789. Springer, Cham. https://doi.org/10.1007/978-3-319-42089-9_38
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DOI: https://doi.org/10.1007/978-3-319-42089-9_38
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