A Study of Stimulative Queen Ant Strategy in Ant Colony Optimization Method

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
_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
_queen
, we propose a new method named "stimulative queen ant strategy AS
^S

_queen
". As experimental results, we have clarified that the AS
^S

_queen
shows better performance than the conventional AS
_queen
in the viewpoint of both "discovery rate of optimal solution" and "average number of iterations"