Scalability of the vector-based Particle Swarm Optimizer

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.