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: 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. © Springer-Verlag Berlin Heidelberg 2008.
Cite
CITATION STYLE
Kovářík, O., & Skrbek, M. (2008). Ant colony optimization with castes. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5163 LNCS, pp. 435–442). https://doi.org/10.1007/978-3-540-87536-9_45
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