Natural Exponential Inertia Weight Strategy in Particle Swarm Optimization

Abstract

Inertia weight is one of the most important parameters of particle swarm optimization (PSO) algorithm. Based on the basic idea of decreasing inertia weight (DIW), two strategies of natural exponential functions were proposed. Four different benchmark functions were used to evaluate the effects of these strategies on the PSO performance. The results of the experiments show that these two new strategies converge faster than linear one during the early stage of the search process. For most continuous optimization problems, these two strategies perform better than the linear one