Principal component particle swarm optimization (PCPSO)

Particle swarm optimization (PSO) is based on the notion of particles flying through solution space. Each particle is assumed to have n-dimensions that are mapped to the variables of the function that is being evaluated. The standard PSO algorithm updates a particle by moving towards the particle´s past personal best and the best particle that has been found. This paper introduces the principal component particle swarm optimization (PCPSO) procedure. The principal component particle swarm optimization procedure flies the particles in two separates spaces at the same time; the traditional n-dimensional x space and a rotated w-dimensional z space where m ≤ n. The Griewank function is used for introducing the PCPSO algorithm and a PCPSO time complexity study.