A Hybrid Model for Solving TSP Based on Artificial Immune and Ant Colony

Artificial immune algorithm has rapid and random overall search ability, but cannot utilize system feedback information sufficiently, which results in redundancy and iteration as well as low solving efficiency. Ant colony algorithm has distributed parallel overall search ability, and can be converged on optimal path by the accumulation and update of information pheromone, but there is a lack of early stage pheromone, and the solving speed is low. This thesis put forth a hybrid algorithm based on artificial immune algorithm and ant colony algorithm, which applies artificial immune algorithm to generate pheromone distribution, and ant colony algorithm for optimal solving. When this algorithm is applied to make computer simulation to solve TSP, it turned out that this algorithm is an optimal method with preferable converging speed and search ability.