The Research And Application Of The Ant Colony Algorithm Based On Artificial Immune Theory

With the development of bionics, artificial intelligence algorithms has become the focus of research, which is widely used in medical, military, environmental protection, and other aspects of life. The ant colony algorithm is the foundation of further study in artificial intelligence algorithms.The ant colony algorithm based on artificial immune theory is a hotspot in simulated evolutionary algorithm, this algorithm shows that fast convergence rate and solving high speed. It is more accuracy. Ant Colony Algorithm converges on the optimal path through pheromone accumulation a renewal, and has the ability of parallel processing and global searching. But its initial solution is stochastic, convergence speed is show and it is easy precocious. The reason is that the movements of the individuals in ant colony are stochastic though they can evolve to the optimal path by interchange information, and they can hardly find a optimum one from a mass of paths within a short time when the problem scale is large enough, eyeless accelerate the convergence speed will make the ants’ local search and lead to premature and stagnation of algorithm easily. Then artificial immune algorithm is a kind of new intelligent optimization algorithm which is inspired by biological immune system. Because this algorithm combines the prior knowledge and the adaptive ability of immune system, is has some characteristic as follow:robust in information processing:not requiring derivable additional information of the objective function in solving optimization problem. Be able to find better global optimal solution in the process of searching.In this paper we propose a hybrid algorithm based on Artificial Immune Algorithm and Ant Colony Algorithm. It adopts Artificial Immune Algorithm to give pheromone to distribute and makes use of Ant Colony Algorithm to give the optimal solution. The computer simulation results show that the proposed algorithm is better than the previous two algorithms on the convergence speed and ability of searching for approximate global optimal solution for solving Traveling Salesman Problem and function optimization problem.