Abstract
AS-PSO, ANT Supervised by PSO is hybrid hierarchical metaheuristic optimization method where PSO optimizes ANT parameters to enhance its performances. In this paper, a focus is made on the impact of the ACO swarm size on AS-PSO performances for the Traveling Salesmen Problem (TSP) where AS-PSO is already known as a relevant solver. Investigations used the AS-PSO-2Opt with both inertia weight AS-PSO and Standard AS-PSO. To demonstrate the effects of ant numbers on AS-PSO-2Opt method, a selected set of test benches form TSPLIB, berlin52, st70 and eli101 was used. In this experimental study of the ant number is waved from five to the city number of each selected test benches. Therefore, experimental results showed that the best swarm size is equal to 20 and gives the best solution for all test benches.
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The authors would like to acknowledge the financial support of this work by grants from General Direction of Scientific Research (DGRST), Tunisia, under the ARUB program.
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Kefi, S., Rokbani, N., Alimi, A.M. (2017). Impact of Ant Size on Ant Supervised by PSO, AS-PSO, Performances. In: Abraham, A., Haqiq, A., Alimi, A., Mezzour, G., Rokbani, N., Muda, A. (eds) Proceedings of the 16th International Conference on Hybrid Intelligent Systems (HIS 2016). HIS 2016. Advances in Intelligent Systems and Computing, vol 552. Springer, Cham. https://doi.org/10.1007/978-3-319-52941-7_56
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