Abstract
Traveling Salesman Problem (TSP) is one of the most widely studied real world problems of finding the shortest (minimum cost) possible route that visits each node in a given set of nodes (cities) once and then returns to origin city. The optimization of cuboid areas has potential samples that can be adapted to real world. Cuboid surfaces of buildings, rooms, furniture etc. can be given as examples. Many optimization algorithms have been used in solution of optimization problems at present. Among them, meta-heuristic algorithms come first. In this study, ant colony optimization, one of meta-heuristic methods, is applied to solve Euclidian TSP consisting of nine different sized sets of nodes randomly placed on a cuboid surface. The performance of this method is shown in tests.
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