Dynamically Updating the Exploiting Parameter in Improving Performance of Ant-Based Algorithms

Abstract

The utilization of pseudo-random proportional rule to balance between the exploitation and exploration of the search process was shown in Ant Colony System (ACS) algorithm. In ACS, this rule is governed by a parameter so-called exploiting parameter which is always set to a constant value. Besides, all ACO-based algorithm either omit this rule or applying it with a fixed value of the exploiting parameter during the runtime of algorithms. In this paper, this rule is adopted with a simple dynamical updating technique for the value of that parameter. Moreover, experimental analysis of incorporating a technique of dynamical updating for the value of this parameter into some state-of-the-art Ant-based algorithms is carried out. Also computational results on Traveling Salesman Problem benchmark instances are represented which probably show that Ant-based implementations with local search procedures gain a better performance if the dynamical updating technique is used.