Ant colony system with selective pheromone memory for TSP

Authors:
Rafał Skinderowicz
Affiliations:
Institute of Computer Science, Silesia University, Sosnowiec, Poland
Venue:
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part II
Year:
2012

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Abstract

Ant Colony System (ACS) is a well known metaheuristic algorithm for solving difficult optimization problems inspired by the foraging behaviour of social insects (ants). Artificial ants in the ACS cooperate indirectly through deposition of pheromone trails on the edges of the problem representation graph. All trails are stored in a pheromone memory, which in the case of the Travelling Salesman Problem (TSP) requires O(n2) memory storage, where n is the size of the problem instance. In this work we propose a novel selective pheromone memory model for the ACS in which pheromone values are stored only for the selected subset of trails. Results of the experiments conducted on several TSP instances show that it is possible to significantly reduce ACS memory requirements (by a constant factor) without impairing the quality of the solutions obtained.