Consideration on Stimulative Processing for Queen Ant Strategy in Swarm Intelligence

Ant colony optimization (ACO) methods, which imitate the pheromone secretion mechanism occurring when ants carry food to their nests, are one of efficient heuristic search methods for combinatorial optimization problems such as traveling salesman problems (TSPs) and so on. In this paper, we analyze the Queen Ant Strategy (AS_queen) that is one of ACO methods in more detail by applying it to six kinds of city-configurations included in the TSPLIB. Furthermore, in order to improve the AS_queen \´s searching ability, we propose a new method which we call the "Stimulative Queen Ant Strategy " or "AS_queen ". Through the experimental evaluation, we clarified that the AS_queen performed better than the conventional AS_queen in both the "discovery rate of optimal solution " and the "average number of iterations before optimal solution was found".