Geometric particle swarm optimization (GPSO) is a recently introduced formal generalization of traditional particle swarm optimization (PSO) that applies naturally to both continuous and combinatorial spaces. In previous work we have developed the theory behind it. The aim of this paper is to demonstrate the applicability of GPSO in practice. We demonstrate this for the cases of Euclidean, Manhattan and Hamming spaces and report extensive experimental results. Categories and Subject Descriptors F.2 [Theory of Computation]: Analysis of Algorithms and Problem Complexity General Terms Theory Keywords Particle Swarm Optimisation, Metric Space, Geometric Crossover