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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

40 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 languageEnglish
Title of host publicationProceedings of 2004 International Conference on Machine Learning and Cybernetics
Place of PublicationNew York, NY, USA
PublisherIEEE Press
Pages2342-2346
Number of pages5
Volume4
ISBN (Print)0780384032
DOIs
Publication statusPublished - Aug 2004

Fingerprint

Traveling salesman problem
Particle swarm optimization (PSO)

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

Cite this

Pang, W., Wang, K., Zhou, C., Dong, L., Liu, M., Zhang, H., & Wang, J. (2004). Modified particle swarm optimization based on space transformation for solving traveling salesman problem. In Proceedings of 2004 International Conference on Machine Learning and Cybernetics (Vol. 4, pp. 2342-2346). New York, NY, USA: IEEE Press. https://doi.org/10.1109/ICMLC.2004.1382191

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 proceedingConference contribution

Pang, W, Wang, K, Zhou, C, Dong, L, Liu, M, Zhang, H & Wang, J 2004, Modified particle swarm optimization based on space transformation for solving traveling salesman problem. in Proceedings of 2004 International Conference on Machine Learning and Cybernetics. vol. 4, IEEE Press, New York, NY, USA, pp. 2342-2346. https://doi.org/10.1109/ICMLC.2004.1382191
Pang W, Wang K, Zhou C, Dong L, Liu M, Zhang H et al. Modified particle swarm optimization based on space transformation for solving traveling salesman problem. In Proceedings of 2004 International Conference on Machine Learning and Cybernetics. Vol. 4. New York, NY, USA: IEEE Press. 2004. p. 2342-2346 https://doi.org/10.1109/ICMLC.2004.1382191
Pang, Wei ; Wang, Kangping ; Zhou, Chunguang ; Dong, Longjiang ; Liu, Ming ; Zhang, Hongyan ; Wang, Jianyu. / Modified particle swarm optimization based on space transformation for solving traveling salesman problem. Proceedings of 2004 International Conference on Machine Learning and Cybernetics. Vol. 4 New York, NY, USA : IEEE Press, 2004. pp. 2342-2346
@inproceedings{ac3ef6ef12cc435e8e5cffb2583e6acb,
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",
month = "8",
doi = "10.1109/ICMLC.2004.1382191",
language = "English",
isbn = "0780384032",
volume = "4",
pages = "2342--2346",
booktitle = "Proceedings of 2004 International Conference on Machine Learning and Cybernetics",
publisher = "IEEE Press",

}

TY - GEN

T1 - Modified particle swarm optimization based on space transformation for solving traveling salesman problem

AU - Pang, Wei

AU - Wang, Kangping

AU - Zhou, Chunguang

AU - Dong, Longjiang

AU - Liu, Ming

AU - Zhang, Hongyan

AU - Wang, Jianyu

PY - 2004/8

Y1 - 2004/8

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.

KW - particle swarm optimization

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 - 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 -