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Author(s): Diogo Duque
José Aleixo Cruz
Henrique Lopes Cardoso
Eugénio Oliveira
Title: Optimizing Meta-heuristics for the Time-Dependent TSP Applied to Air Travels
Issue Date: 2018
Abstract: A travel agency has recently proposed the Traveling Salesman Challenge (TSC), a problem consisting of finding the best flights to visit a set of cities with the least cost. Our approach to this challenge consists on using a meta-optimized Ant Colony Optimization (ACO) strategy which, at the end of each iteration, generates a new ant by running Simulated Annealing or applying a mutation operator to the best ant of the iteration. Results are compared to variations of this algorithm, as well as to other meta-heuristic methods. They show that the developed approach is a better alternative than regular ACO for the time-dependent TSP class of problems, and that applying a K-Opt optimization will usually improve the results. (c) 2018, Springer Nature Switzerland AG.
Source: Intelligent Data Engineering and Automated Learning - IDEAL 2018 - 19th International Conference, Madrid, Spain, November 21-23, 2018, Proceedings, Part I
Document Type: Artigo em Livro de Atas de Conferência Internacional
Rights: openAccess
Appears in Collections:FEUP - Artigo em Livro de Atas de Conferência Internacional

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