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 › Published conference contribution

53 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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10.1109/ICMLC.2004.1382191

Cite this

Modified particle swarm optimization based on space transformation for solving traveling salesman problem. / Pang, Wei; Wang, Kangping; Zhou, Chunguang et al.
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 › Published conference contribution

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title = "Modified particle swarm optimization based on space transformation for solving traveling salesman problem",

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.",

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",

author = "Wei Pang and Kangping Wang and Chunguang Zhou and Longjiang Dong and Ming Liu and Hongyan Zhang and Jianyu Wang",

year = "2004",

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doi = "10.1109/ICMLC.2004.1382191",

language = "English",

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AU - Liu, Ming

AU - Zhang, Hongyan

AU - Wang, Jianyu

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N2 - 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.

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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KW - traveling salesman problem

KW - chaotic operations

KW - local search

KW - benchmark testing

KW - chaos

KW - educational institutions

KW - random number generation

KW - space exploration

KW - evolutionary computation

KW - search problems

U2 - 10.1109/ICMLC.2004.1382191

DO - 10.1109/ICMLC.2004.1382191

M3 - Published conference contribution

SN - 0780384032

VL - 4

SP - 2342

EP - 2346

BT - Proceedings of 2004 International Conference on Machine Learning and Cybernetics

PB - IEEE Press

CY - New York, NY, USA

ER -

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

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Keywords

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