Hybrid Optimization Algorithm Based on Ant Colony and Fish School

This paper proposes a hybrid optimization algorithm to resolve combinatorial optimization problem. Aswarm degree in the artificial fish school algorithm is used in ant colony algorithm. During the initial process of the optimization, the aswarm 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 aswarm 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 results prove the validity of the algorithm.