Abstract
In this paper we apply a multi-caste ant colony system to the dynamic traveling salesperson problem. Each caste inside the colony contains its own set of parameters, leading to the coexistence of different exploration behaviors. Two multi-caste variants are proposed and analyzed. Results obtained with different dynamic scenarios reveal that the adoption of a multi-caste architecture enhances the robustness of the algorithm. A detailed analysis of the outcomes suggests guidelines to select the best multi-caste variant, given the magnitude and severity of changes occurring in the dynamic environment.
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Melo, L., Pereira, F., Costa, E. (2013). Multi-caste Ant Colony Algorithm for the Dynamic Traveling Salesperson Problem. In: Tomassini, M., Antonioni, A., Daolio, F., Buesser, P. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2013. Lecture Notes in Computer Science, vol 7824. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37213-1_19
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DOI: https://doi.org/10.1007/978-3-642-37213-1_19
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