Particle swarm optimization-based algorithms for TSP and generalized TSP

Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.ipl.2007.03.010

Title:	Particle swarm optimization-based algorithms for TSP and generalized TSP
Authors:	Shi, X.H. Liang, Y.C. Lee, H.P. Lu, C. Wang, Q.X.
Keywords:	Algorithms Generalized traveling salesman problem Particle swarm optimization Swap operator Traveling salesman problem
Issue Date:	31-Aug-2007
Citation:	Shi, X.H., Liang, Y.C., Lee, H.P., Lu, C., Wang, Q.X. (2007-08-31). Particle swarm optimization-based algorithms for TSP and generalized TSP. Information Processing Letters 103 (5) : 169-176. ScholarBank@NUS Repository. https://doi.org/10.1016/j.ipl.2007.03.010
Abstract:	A novel particle swarm optimization (PSO)-based algorithm for the traveling salesman problem (TSP) is presented. An uncertain searching strategy and a crossover eliminated technique are used to accelerate the convergence speed. Compared with the existing algorithms for solving TSP using swarm intelligence, it has been shown that the size of the solved problems could be increased by using the proposed algorithm. Another PSO-based algorithm is proposed and applied to solve the generalized traveling salesman problem by employing the generalized chromosome. Two local search techniques are used to speed up the convergence. Numerical results show the effectiveness of the proposed algorithms. © 2007 Elsevier B.V. All rights reserved.
Source Title:	Information Processing Letters
URI:	http://scholarbank.nus.edu.sg/handle/10635/61051
ISSN:	00200190
DOI:	10.1016/j.ipl.2007.03.010
Appears in Collections:	Staff Publications

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