Alternative pheromone laying strategy — An improvement for the ACO algorithm

Abstract

This paper gives a brief overview of the current state in the ant colony optimization (ACO) field of study. Furthermore, it introduces an alternative pheromone laying strategy for the ACO algorithm. In the paper, the newly introduced strategy is implemented, tested on a model problem and compared with the classical approach. A parameterized problem space generator has been introduced. The generator generates graphs along which ants are allowed to move freely on the Y axis, but constrained to increment the current value on the X axis by one with each move. In this way, a dynamic decision making optimization problem with the goal of minimizing the path from an arbitrary starting node to an arbitrary finish node has been simulated. Using the ACO algorithm, the generated problems are being solved with the classical pheromone laying approach and the modified approach, introduced in this paper. The obtained results unequivocally indicate that the introduced modification has the potential to serve as an improvement for the ACO algorithm in general.