A Modified PSO Using Great Deluge Algorithm for Optimization

A modified Particle Swarm Optimization (PSO) using Great Deluge Algorithm (GDA) called MPSO for optimization is presented in this paper. In the proposed algorithm, the range for achieved answers is defined that is the same parameter used in the GDA called “water level”. Amount of this range reduces or increases regarding to algorithm’s property being used in terms of minimum or maximum during the time. Difference of the proposed algorithm with previous PSOs is that, particles are given a second chance using GDA. So if a particle is trapped in the local optimum may get rid of it. New algorithm has been tested on some standard functions and its performance has been compared with standard PSO. Test results indicate that the proposed method significantly improves the ability of PSO of escaping from the local optimal raise and increases the accuracy and the convergence rate.