A Modified Particle Swarm Optimization Algorithm

Abstract

A modified particle swarm optimization algorithm is proposed in this paper. In the presented algorithm, every particle chooses its inertial factor according to the approaching degree between the fitness of itself and the optimal particle. Simultaneously, a random number is introduced into the algorithm in order to jump out from local optimum and a minimum factor is used to avoid premature convergence. Five well-known functions are chosen to test the performance of the suggested algorithm and the influence of the parameter on performance. Simulation results show that the modified algorithm has advantage of global convergence property and can effectively alleviate the problem of premature convergence. At the same time, the experimental results also show that the suggested algorithm is greatly superior to PSO and APSO in terms of robustness.