Geometric particle swarm optimisation on binary and real spaces: from theory to practice

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.

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