Abstract
Simulated annealing (SA) is a well-known metaheuristic commonly used to solve a great variety of \(\mathcal {NP}\)-hard problems such as the quadratic assignment problem (QAP). As commonly known, the choice and size of neighborhoods can have a considerable impact on the performance of SA. In this work, we investigate and propose a SA variant that considers variable neighborhood structures driven by the state of the search. In the computational experiments, we assess the contribution of this SA variant in comparison with the state-of-the-art SA for the QAP applied to printed circuit boards and conclude that our approach is able to report better solutions by means of short computational times.
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Lalla-Ruiz, E., Heilig, L., Voß, S. (2020). Assessing Simulated Annealing with Variable Neighborhoods. In: Matsatsinis, N., Marinakis, Y., Pardalos, P. (eds) Learning and Intelligent Optimization. LION 2019. Lecture Notes in Computer Science(), vol 11968. Springer, Cham. https://doi.org/10.1007/978-3-030-38629-0_24
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DOI: https://doi.org/10.1007/978-3-030-38629-0_24
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