Multi agent routing to multi targets via ant colony

Ant colony optimization (ACO) is a cooperative, population-based technique for optimization. Ant algorithms were designed on the base of the behavior of real ant colonies. Real ants can always find the shortest way between the nest and food source, using the environment as the communication tool, named stigmergy. In this paper we focus precisely on the process of finding an optimal path by ant colony optimization algorithm in the multi agent environment. Rescue Simulation, one of the fields of RoboCup competitions, was chosen as the test bed. We have proposed this algorithm to find the best path between basis and destination. Calculating the `reachablitiy' of a set of targets is the main problem which should be solved with constraints like uncertainty and time limits. Some characteristics of ACO which is utilized as a social algorithm encouraged us to implement it in such problems. Numerical result comparisons with A* algorithm demonstrate the effectiveness and efficiency of this algorithm in Rescue Simulation Environment. The bright background of our team in several world competitions is another support of this work.