Abstract
The Particle Swarm Optimization is one of the most famous nature inspired algorithm that belongs to the swarm optimization family. It has already been used successfully in the continuous problem. However, this algorithm has not been explored enough for the discrete domain. In this work we introduce new operators that are dedicated to combinatorial research that we implemented on a modified discrete particle swarm optimization called DPSO-CO to solve travelling salesman problem. The experimental results on a set of different instances, and the comparison study with others adaptations show that adopting new ways, combinations and operators gives birth to a really competitive efficient algorithm in operational research.
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Bouzidi, M., Riffi, M.E., Serhir, A. (2018). Discrete Particle Swarm Optimization for Travelling Salesman Problems: New Combinatorial Operators. 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_14
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DOI: https://doi.org/10.1007/978-3-319-76357-6_14
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