Modified particle swarm optimization based on space transformation for solving traveling salesman problem

Wei Pang; Kangping Wang; Chunguang  Zhou; Longjiang Dong; Ming Liu; Hongyan Zhang; Jianyu Wang

doi:10.1109/ICMLC.2004.1382191

Modified particle swarm optimization based on space transformation for solving traveling salesman problem

Wei Pang, Kangping Wang, Chunguang Zhou, Longjiang Dong, Ming Liu, Hongyan Zhang, Jianyu Wang

Computing Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

43 Citations (Scopus)

Abstract

A modified particle swarm optimization was proposed to solve traveling salesman problem (TSP). The algorithm searched in the Cartesian continuous space, and constructed a mapping from continuous space to discrete permutation space of TSP, thus to implement the space transformation. Moreover, local search technique was introduced to enhance the ability to search, and chaotic operations were employed to prevent the particles from falling into local optima prematurely. Finally four benchmark problems in TSPLIB were tested to evaluate the performance of the algorithm. Experimental results indicate that the algorithm can find high quality solutions in a comparatively short time.

Original language	English
Title of host publication	Proceedings of 2004 International Conference on Machine Learning and Cybernetics
Place of Publication	New York, NY, USA
Publisher	IEEE Press
Pages	2342-2346
Number of pages	5
Volume	4
ISBN (Print)	0780384032
DOIs	https://doi.org/10.1109/ICMLC.2004.1382191
Publication status	Published - Aug 2004

Keywords

particle swarm optimization
traveling salesman problem
chaotic operations
local search
benchmark testing
chaos
educational institutions
random number generation
space exploration
evolutionary computation
search problems

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Modified particle swarm optimization based on space transformation for solving traveling salesman problem. / Pang, Wei; Wang, Kangping; Zhou, Chunguang ; Dong, Longjiang; Liu, Ming; Zhang, Hongyan; Wang, Jianyu.

Proceedings of 2004 International Conference on Machine Learning and Cybernetics. Vol. 4 New York, NY, USA : IEEE Press, 2004. p. 2342-2346.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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abstract = "A modified particle swarm optimization was proposed to solve traveling salesman problem (TSP). The algorithm searched in the Cartesian continuous space, and constructed a mapping from continuous space to discrete permutation space of TSP, thus to implement the space transformation. Moreover, local search technique was introduced to enhance the ability to search, and chaotic operations were employed to prevent the particles from falling into local optima prematurely. Finally four benchmark problems in TSPLIB were tested to evaluate the performance of the algorithm. Experimental results indicate that the algorithm can find high quality solutions in a comparatively short time.",

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AB - A modified particle swarm optimization was proposed to solve traveling salesman problem (TSP). The algorithm searched in the Cartesian continuous space, and constructed a mapping from continuous space to discrete permutation space of TSP, thus to implement the space transformation. Moreover, local search technique was introduced to enhance the ability to search, and chaotic operations were employed to prevent the particles from falling into local optima prematurely. Finally four benchmark problems in TSPLIB were tested to evaluate the performance of the algorithm. Experimental results indicate that the algorithm can find high quality solutions in a comparatively short time.

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