The TSP Optimization Algorithm Based On The Ant Colony Algorithm

Ant Colony Algorithm (ACA) is one kind of bionics optimization algorithm, which simulates the kingdom of ant community to look for food in the recent development. Though it has demonstrated excellent performance and tremendous development potential to solve many complex combinatorial optimization problem, it is not mature enough to solve the practical problems so that there still exits much development space. After studying a great amount of relevant literatures, some possibility methods of the ACA improvements are proposed in this paper to correct some flaws in solving the practical combinatorialoptimization problems with current ACA.The main research works are that the algorithm model is improved on the basis of theexperiential analysis of ACA, On the term of pheromone mechanism, the paper firstly introduce the concept "pheromone diffusion" to better consider the previous node to avoid the unnecessary searching; The ACA based on pheromone diffusion has the ability of acquire the best solution, and pheromone diffusion make the ACA obtain the best global solution, that it will uneasily replace into the best partial solution. When solving the practical TSP, all node coordinate is treated preliminarily at first, secondly using pheromone diffusion mechanism and ant inundation to correct the model of ACA, in order to find the shortest and cheapest path in the same iteration.At last, the experiment result show the high efficiency of the improved ACA by compile programmer in VC environment, and demonstrate the feasibility of proposed method which solve the TSP.