Abstract
The minimum latency problem is a variant of the well-known travelling salesperson problem where the objective is to minimize the sum of arrival times at vertices. Recently, a proposal that incorporates data mining into a state-of-the-art metaheuristic by injecting patterns from high-quality solutions has consistently led to improved results in terms of solution quality and running time for this problem. This paper extends that proposal by leveraging data mining to contract portions of the problem frequently found in high-quality solutions. Our proposal aims at mitigating the burden of searching for improving solutions by periodically solving a reduced version of the original problem. Computational experiments conducted on a well-diversified set of instances demonstrate that our proposal improved solution quality without increasing computational time, introducing 11 new best solutions to the literature.
This work was supported by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq, Brazil) [grants 310444/2018-7, 310624/2018-5, 309832/2020-9], Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ, Brazil) [grant E-26/201.344/2021], Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES, Brazil) [grant 88887.646206/2021-00], and Instituto Brasileiro de Geografia e Estatística (IBGE, Brazil).
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Maia, M.R.d.H., Santana, Í., Rosseti, I., Souza, U.d.S., Plastino, A. (2023). MineReduce-Based Metaheuristic for the Minimum Latency Problem. In: Di Gaspero, L., Festa, P., Nakib, A., Pavone, M. (eds) Metaheuristics. MIC 2022. Lecture Notes in Computer Science, vol 13838. Springer, Cham. https://doi.org/10.1007/978-3-031-26504-4_7
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