Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Adaptively choosing niching parameters in a PSO
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Containing particles inside niches when optimizing multimodal functions
SAICSIT '05 Proceedings of the 2005 annual research conference of the South African institute of computer scientists and information technologists on IT research in developing countries
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
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This paper presents an investigation into the scalability of the vector-based PSO, a niching algorithm using particle swarm optimization. The vector-based PSO locates and maintains niches by using vector operations to determine niche boundaries. The technique builds upon existing knowledge of the particle swarm in such a way that the swarm can be organized into subswarms without prior knowledge of the number of niches in the search space and the corresponding niche radii, thus reducing the number of user-specified parameters. In a designated search space a linear increase in the number of dimensions often results in an exponential or near exponential increase in the number of optima. Empirical results are reported where the vector-based PSO is tested on three multimodal functions in one to four dimensions using a range of swarm sizes. Optimal swarm sizes are derived where all or most of the optima should be located.