A Self-Organizing Particle Swarm Optimization Algorithm and Application

Abstract

A self-organizing particle swarm optimization algorithm is developed for solving premature convergence of particle swarm optimization. According to adaptively adjusting acceleration coefficients and inertia weight, the particles are organized to track the domain of attraction of local optimum and the domain of attraction global optimum respectively during the search. Meanwhile the corresponding strategies with mutation are adopted in different stages of this algorithm to further enhance diversity of population. Experimental results for complex function optimization and nonlinear system identification show that this algorithm improves the global convergence ability and efficiently prevents the algorithm from the local optimization and early maturation.