Particle Swarm Optimization Algorithm with Exponent Decreasing Inertia Weight and Stochastic Mutation

Abstract

The paper gives an improved particle swarm optimal algorithm in which a kind of exponent decreasing inertia weights is given to improve the convergence speed and a kind of stochastic mutations is used to improve the diversity of the swarm in order to overcome the disadvantage of premature convergence and later period oscillatory occurrences. It is shown by five representative benchmarks functionpsilas test that the improved algorithm is better than both a particle swarm optimization with linear decreasing inertia weight and a particle swarm optimization with exponent decreasing inertia weight in global searching and performance.