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 | |
| Publication status | Published - 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