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A Hybrid Particle Swarm Optimization Method for Traveling Salesman Problem

A Hybrid Particle Swarm Optimization Method for Traveling Salesman Problem

Yong Wang, Ning Xu
Copyright: © 2017 |Volume: 8 |Issue: 3 |Pages: 13
ISSN: 1947-8283|EISSN: 1947-8291|EISBN13: 9781522513230|DOI: 10.4018/IJAMC.2017070104
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MLA

Wang, Yong, and Ning Xu. "A Hybrid Particle Swarm Optimization Method for Traveling Salesman Problem." IJAMC vol.8, no.3 2017: pp.53-65. http://doi.org/10.4018/IJAMC.2017070104

APA

Wang, Y. & Xu, N. (2017). A Hybrid Particle Swarm Optimization Method for Traveling Salesman Problem. International Journal of Applied Metaheuristic Computing (IJAMC), 8(3), 53-65. http://doi.org/10.4018/IJAMC.2017070104

Chicago

Wang, Yong, and Ning Xu. "A Hybrid Particle Swarm Optimization Method for Traveling Salesman Problem," International Journal of Applied Metaheuristic Computing (IJAMC) 8, no.3: 53-65. http://doi.org/10.4018/IJAMC.2017070104

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Abstract

Traveling salesman problem (TSP) is one well-known NP-Complete problem. The objective is to search the optimal Hamiltonian circuit (OHC) in a tourist map. The particle swarm optimization (PSO) integrated with the four vertices and three lines inequality is introduced to detect the OHC or approximate OHC. The four vertices and three lines inequality is taken as local heuristics to find the local optimal paths composed of four vertices and three lines. Each of this kind of paths in the OHC or approximate OHC conforms to the inequality. The particle swarm optimization is used to search an initial approximation. The four vertices and three lines inequality is applied to convert all the paths in the approximation into the optimal paths. Then a better approximation is obtained. The method is tested with several Euclidean TSP instances. The results show that the much better approximations are searched with the hybrid PSO. The convergence rate is also faster than the traditional PSO under the same preconditions.

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