Particle swarm optimization with controlled mutation

Abstract

Abstract This paper presents a new Particle Swarm Optimization (PSO) technique which uses mutation. This method is based on PSO, with mutation appended to it. The most important point in this paper is that we make use of the information provided to gbest by the mutation of just one dimension, because we consider that gbest is the best possible information to update after mutation. In this paper, we adapt this method into Linearly Decreasing Inertia Weight Method (LDIWM), and validate it through several benchmark problems. Copyright © 2007 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.