Ant Colony Optimization (ACO) methods, which imitate a mechanism of pheromone secretion when ants carry food to their nest, are one of efficient heuristic search methods for combinational optimization problems such as traveling salesman problems (TSPs) and so on. In this paper, we analyze the Queen Ant Strategy AS{sub}(queen) that is one of ACO methods more in detail by applying it to six kinds of city configurations included in the TSPLIB. Furthermore, in order to improve searching ability of the AS{sub}(queen), we propose a new method named "Stimulative Queen Ant Strategy AS{sub}(queen){sup}S". As experimental results, we have clarified that the AS{sub}(queen){sup}S shows better performance than the conventional AS{sub}(queen) in the viewpoint of both "discovery rate of optimal solution" and "average number of iterations".
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