Abstract
The Mother Tree Optimization (MTO) algorithm is a new swarm intelligence technique that we have recently proposed for solving continuous optimization problems. MTO is built on an offspring topology and a set of cooperating agents. In this paper, we first present a discrete version of MTO, that we call Discrete MTO (DMTO), for solving the Traveling Salesman Problem (TSP). DMTO is based on a new swap operation that is best suited for TSPs. We also used this swap operation to introduce an updated version of the Discrete Particle Swarm Optimization (DPSO) algorithm. With a careful application of our new swap operation, we will show that our Updated DPSO (UDPSO) is more effective than DPSO. In order to assess the performance of our proposed methods, DMTO and UPSO, we conducted several experiments comparing both to DPSO and an exact method (Branch and Bound), on ten TSP instances taken from the well-known TSPLIB dataset. The results clearly show that DMTO produces solutions of much better quality than in DPSO, and superior to those in UDPSO.
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Korani, W., Mouhoub, M. (2020). Discrete Mother Tree Optimization for the Traveling Salesman Problem. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Lecture Notes in Computer Science(), vol 12533. Springer, Cham. https://doi.org/10.1007/978-3-030-63833-7_3
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