Gaussian swarm: A novel particle optimization algorithm

In this paper, a novel particle swarm optimization algorithm based on the Gaussian probability distribution is proposed. The standard Particle Swarm optimization (PSO) algorithm has some parameters that need to be specified before using the algorithm, e.g., the accelerating constants c(1) and c(2), the inertia weight w, the maximum velocity V-max, and the number of particles of the swarm. The purpose of this work is the development of an algorithm based on the Gaussian distribution, which improves the convergence ability of PSO without the necessity of tuning these parameters. The only parameter to be specified by the user is the number of particles. The Gaussian PSO algorithm was tested on a suite of well-known benchmark functions and the results were compared with the results of the standard PSO algorithm. The simulation results shows that the Gaussian Swarm outperforms the standard one.