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.
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