Abstract
Ant Colony Algorithm (ACA) is a kind of heuristic search algorithm based on population search. As a new bionic algorithm, ACA has attracted a lot of experts and scholars' attention. And they deduct many different improved versions. Because of the advantages of ant colony algorithm in solving combinatorial optimization problems, it has been widely applied to various fields such as academic, industrial and commercial fields. Traveling salesman problem (TSP) is one of the classical problems in the field of combinatorial optimization. The traditional ant colony algorithm is early used to solve the TSP problem, but it is limited by its own defects such as slow convergence, easy to fall into the local optimal solution and so on. This paper improves the traditional ant colony algorithm, and it is applied to solve the TSP problem. First of all, we use genetic algorithm to improve ant colony algorithm. By introducing the mutation factor is in the algorithm, we can adjust the numerical value of the mutation factor with the algorithm. Therefore, in the initial stage of the algorithm, we can guarantee that the search scope of the ant colony is large enough to prevent the local convergence of the algorithm. Secondly, in order to improve the operation speed of the algorithm, we can optimize the operation process by uniform design in the initial stage of the algorithm. Finally, by integrating these two methods together, we use the improved ant colony algorithm to solve the TSP problem effectively.
