Particle Swarm Optimization with Gaussian Disturbance

Particle Swarm Optimization (PSO) is a population-based heuristic algorithm with fast speed in many complicated problems. However PSO easily falls into local optima because of the attraction from the best particle in velocity updating formula. This paper proposes an improved PSO with Gaussian disturbance (GDPSO) to solve this problem by adding a Gaussian chaos on the best particle to lead the swarm flies away from dilemma. Experimental results on some well-known benchmark problems have shown that GDPSO could successfully handle with those difficult functions while maintaining fast search speed.