Pheromone-distribution-based adaptive ant colony system

Abstract

Parameters values have significant effects on the performance of the ant colony system (ACS) algorithm. However, it is a difficult task to choose proper parameters values for achieving the best performance of the algorithm. That is because the best parameters values are not only dependent on specific problems, but also related to the optimization states during the search process. This paper proposes a novel adaptive parameters control scheme for ACS and develops an adaptive ACS (AACS) algorithm. Different from the existing parameters control schemes, the parameters values in AACS are adaptively controlled according to the current optimization state, which is estimated based on measuring the pheromone trails distribution. The proposed AACS algorithm is applied to solve a series of benchmark traveling salesman problems (TSPs). The resulting solution quality and the convergence rate of AACS are favorably compared with the results by the ACS using fixed parameters values and two existing adaptive parameters control methods. Experimental results show that our proposed method is effective and competitive. Copyright 2010 ACM.