A hybrid particle swarm optimization model for the traveling salesman problem
This work presents a new hybrid model, based on Particle Swarm Optimization, Genetic Algorithms and Fast Local Search, for the symmetric blind traveling salesman problem. A detailed description of the model is provided. The implemented system was tested with instances from 76 to 2103 cities. For instances up to 439 cities, results were, in average, less than or around 1% in excess of the known optima. When considering all instances, results were 2.1498% in excess, in average. These excellent results encourage further research and improvement of the hybrid model.
KeywordsLocal Search Hybrid Model Travel Salesman Problem Travel Salesman Problem Activation Vector
Unable to display preview. Download preview PDF.
- Goldberg, D.E. (1989) Genetic Algorithms in Search, Optimization and Machine Learning, Addison Wesley, ReadingGoogle Scholar
- Wang, K.P., Huang, L., Zhou, C.G., Pang, W. (2003) Particle swarm optimization for traveling salesman problem. In: Proc. 2nd IEEE Int. Conf. on Machine Learning and Cybernetics, pp. 1583–1585Google Scholar
- Løvbjerg, M., Rasmussen, T. K., Krink T. (2001) Hybrid particle swarm optimizer with breeding and subpopulations. In: Proc. Genetic and Evolutionary Computation Conf., pp.469–476Google Scholar