Ant Colony Optimization with Castes

Authors:
Oleg Kovářík;Miroslav Skrbek
Affiliations:
Department of Computer Science and Engineering, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic 121 35;Department of Computer Science and Engineering, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic 121 35
Venue:
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Year:
2008

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Abstract

Ant Colony Optimization (ACO) is a nature inspired metaheuristic for solving optimization problems. We present a new general approach for improving ACO adaptivity to problems, Ant Colony Optimization with Castes (ACO+C). By using groups of ants with different characteristics, known as castes in nature, we can achieve better results and faster convergence thanks to possibility to utilize different types of ant behaviour in parallel. This general principle is tested on one particular ACO algorithm: ${\cal MAX}-{\cal MIN}$ Ant System solving Symmetric and Asymmetric Travelling Salesman Problem. As experiments show, our method brings a significant improvement in the convergence speed as well as in the quality of solution for all tested instances.