Abstract
Self-adapting heuristics is a very challenging research issue allowing setting a class of solvers able to overcome complex optimization problems without being tuned. Ant supervised by PSO, AS-PSO, as well as its simplified version SASPSO was proposed in this scope. The main contribution of this paper consists in coupling the simplified AS-PSO with a local search mechanism and its investigations over standard test benches, of TSP instances. Results showed that the proposed method achieved fair results in all tests: find the best-known solution or even find a better one essentially for the following cases: eil51, berlin52, st70, KroA100 and KroA200. The proposed method turns better results with a faster convergence time than the classical Ant Supervised by PSO and the standard Ant Supervised by PSO as well as related solvers essentially for eil51, berlin52, st70 and kroA100 TSP test benches.
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Twir, I., Rokbani, N., Haqiq, A., Abraham, A. (2018). Experimental Investigation of Ant Supervised by Simplified PSO with Local Search Mechanism (SAS-PSO-2Opt). In: Abraham, A., Haqiq, A., Muda, A., Gandhi, N. (eds) Proceedings of the Ninth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2017). SoCPaR 2017. Advances in Intelligent Systems and Computing, vol 737. Springer, Cham. https://doi.org/10.1007/978-3-319-76357-6_17
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