An optimization algorithm based on ant colony algorithm

This paper proposes an optimization algorithm to resolve combinatorial optimization problem. Congestion degree in the artificial fish school algorithm is used in ant colony algorithm in this paper. During the initial process of the optimization, the congestion degree plays the main role to guide the ants to search the new path randomly, which makes the algorithm have the stronger ergodicity searching ability. The role of the congestion degree gradually decreases to zero, the algorithm becomes the conventional ant colony and completes the optimal process by the principle of pheromone positive feedback, which insures the algorithm to have a quick convergence rate. Simulation experiment result shows this algorithm not only overcomes possible problem such as prematurity phenomenon in the early period of the ant colony algorithm, and enhances algorithm the ability of traversal optimization, but also has fast convergence speed, and improves optimal performance. This algorithm is better than the adaptive ant colony algorithm and the adaptive fish school algorithm.