A Modified Ant System using Gaussian Probabilistic Pheromone Updation Technique

Vol.6 No.4

Year: 2012

Issue: April-June

Title: A Modified Ant System using Gaussian Probabilistic Pheromone Updation Technique                                

Author Name: Anirban Pal, Debarghya Das, Abhishek Paul   

Synopsis:   

Ant Colony Optimization (ACO) is mainly inspired by the foraging behavior of ants. In this paper, we have proposed a modified model for ant system, entitled as Gaussian Probabilistic Ant System (GPAS) for probabilistic pheromone updating. This proposed algorithm is implemented by incorporating a probabilistic property in the pheromone trail deposition factor, stated as ? (rho).We use the equation proposed by Karl Friedrich Gauss, well-known mathematician and physical scientist, in our GPAS, for updating ?. Trail deposition factor, ?, is in general a static factor and here it has been made probabilistic so as to increase the effectiveness of the ant system in finding the optimal tour for Traveling Salesman Problem (TSP). GPAS modifies its properties in accordance to the requirement of surrounding domain and for the betterment of its performance in dynamic environment. The experimental evaluation conducted to find out the usefulness of the new strategy, using selective benchmark problems from TSP library [6]. Our algorithm shows effective and comparable results as compared to other existing approaches.