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

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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