Abstract
We have known the effectiveness for an algorithm of Ant Colony Optimization (ACO) and it has applied some applications. Real ants have communicated each other with pheromone materials and they search the shortest path from their colony to the place of foods. Researchers have reported the effectiveness of the algorithm for their applications. However there are some problems to resolve using this algorithm. One of them is a tradeoff problem between a convergence performance and a diversity of candidate for the optimized solution.
On the other hand, from observations for real ants and their colonies, researchers in the field of biology have reported that there are two types of ants in the colonies. One of them is hardworking ants and another type is not hardworking ants. Then we have introduced different types of agents with ACO and we aim to generate stigmergy between agents. We have done some evaluation experiments in RoboCup Rescue Simulation System. From the results, we have considered the results between ACO with mono-agents and ACO with some types of agents. We have confirmed the effectiveness of our proposed method.
