Immune Ant Colony Algorithm And Its Application

Artificial Immune Algorithm has the ability of doing a global searching quickly and stochastically. But it can't make use of enough output information, and hence do a large redundancy repeat searching for the optimal solution, which reduces the efficiency of algorithm. Ant Colony Algorithm converges on the optimal path through pheromone accumulation an renewal, and has the ability of parallel processing and global searching. But its initial solution is stochastic, convergence speed is slow and it is easy precocious. 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 problems.