A Novel Evolutionary Strategy for Particle Swarm Optimization

A novel evolutionary strategy for Particle swarm optimization (PSO) to enhance the convergencespeed and avoid the local optima is presented. The positive experience and negative lesson from the individualparticle's cognition and the swarm's social knowledge areused to accumulate the system's intelligence and guidethe swarm's evolution behaviors. The new generation ofswarms (named as Child Swarm) and the adjacent formerswarms (named as Parent Swarm) are mixed to select thesurvival of the fittest. The eliminated particles are replaced by the random particles from the outside surroundings. Darwinian evolution method contributes to the convergence and the durative interactions between the swarmsand the surroundings who contribute to the global search.This new method can converges faster, gives more robustand precise result and can prevent prematurity more effectively. The corresponding simulation results are presented.