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.
CITATION STYLE
Duque, D., Cruz, J. A., Cardoso, H. L., & Oliveira, E. (2018). Optimizing Meta-heuristics for the Time-Dependent TSP Applied to Air Travels. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11314 LNCS, pp. 730–739). Springer Verlag. https://doi.org/10.1007/978-3-030-03493-1_76
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