Abstract
The paper discusses different approaches to parallel implementation of the Ant Colony Optimization metaheuristic. The metaheuristic is applied to the well-known Travelling Salesman Problem. Although the Ant Colony Optimization approach is capable of delivering good quality solutions for the TSP it suffers from two factors: complexity and non-determinism. Overpopulating ants makes the ACO performance more predictable but increasing the number of ants makes the need for parallel processing even more apparent. The proposed Ant Colony Community (ACC) uses a coarse grained approach to parallelization. Two implementations using RMI and Sockets respectively are compared. Results of an experiment prove the ACC is capable of a substantial reduction of processing time.
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Siemiński, A. (2016). Parallel Implementations of the Ant Colony Optimization Metaheuristic. In: Nguyen, N.T., Trawiński, B., Fujita, H., Hong, TP. (eds) Intelligent Information and Database Systems. ACIIDS 2016. Lecture Notes in Computer Science(), vol 9621. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49381-6_60
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DOI: https://doi.org/10.1007/978-3-662-49381-6_60
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