In this paper, we prompt a new multi-dimensional algoithm to solve the traveling salesman problem based onthe ant colony optimization algorithm and genetic algorithm. Ant Colony Optimization (ACO) is a heuristic algorithmwhich has been proven a successful technique and applied to a number of combinatorial optimization (CO) problems. Thetraveling salesman problem (TSP) is one of the most important combinatorial problems. ACO is taken as one of the highperformance computing methods for TSP. It still has some drawbacks such as stagnation behavior, long computationaltime, and premature convergence problem of the basic ACO algorithm on TSP. Those problems will be more obviouswhen the considered problems size increases. The proposed system based on basic ACO algorithm with well distributionstrategy and information entropy which is conducted on the configuration strategy for updating the heuristic parameter inACO to improve the performance in solving TSP. Then, ACO for TSP has been improved by incorporating local optimizationheuristic. Algorithms are tested on benchmark problems from TSPLIB and test results are presented. From our experiments,the proposed algorithm has better performance than ACO algorithm.
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