TSP Problem Based On Ant Colony Algorithm

Ant colony algorithm is a kind of bionic optimization algorithms, which is inspired by theant foraging behavior. Ant colony algorithm has strong robustness, because of using distributedcomputing system and combining with other methods easily. Furthermore, It has been applied tomany fields with good results. In fact, TSP problem is a NP problem. How to improve theefficiency of TSP problem solving has a lot of theoretical and practical significance. Ant colonyalgorithm can be better applied to the TSP problem solving.This paper first introduces the ant colony algorithm and the knowledge of the TSP problemand Research, on the basis of the existing ant colony algorithm theory and research, we proposean improved ant colony algorithm, the author improves the ant colony algorithm, and thefollowing work is carried out:(1)Introducing, analyzing and summarizing mathematical model and several main methodsfor solving TSP problem. Compared with several classic bionic optimization algorithms, andbased on this, the author introduces the mathematical model and implement process of ant colonyalgorithm, explains several classic optimization algorithm.(2)Conforming to the ant colony algorithm parameters effects, the author focus on thenumber of ants, pheromone residue coefficient, heuristic factor, intensity of pheromone of antcolony algorithm. Because of many of parameters, there are a thousand choice in a thousandpeople’s eyes. Therefore, This is the key in finding the balance between Study and application.Not only making the search space as large as possible, but also using valid information to explorepowerful global searching. The author use experimental problems as subjects in the tsplib, byanalysising on the main parameter. According to associated settings, autor gives some discussionsto the m、、、、Q.With Ant-Cycle model as an example, autor obtains the optimumsolutions.(3) A detailed study of some existing improved ant colony algorithm, and through theTSPLIB test library of experimental problems subjects to improved ant colony algorithm testing,from the analysis of experimental results of these improved ant colony algorithm performance ofthe pros and cons. Focus on the polymorphism of ant colony algorithm, adaptive adjustmentpheromone volatile factor ant colony algorithm, based on local optimization strategy to cross theant colony algorithm based on mixture and the behavior of the ant colony algorithm of severa lclassic improved ant colony algorithm. Combined with the several improved ant colonyalgorithm of ant colony algorithm and related parameters of the research, given here in a majorbased on the polymorphism of ant colony algorithm improved ant colony algorithm, the improved algorithm are given the ideas, algorithm steps, eventually, through the same experiment theperformance of the algorithm, prove feasibility and effectiveness of the algorithm.