Particle Swarm Optimization driven by Evolving Elite Group

This paper proposes a novel hybrid algorithm of particle swarm optimization (PSO) and evolutionary programming (EP), named particle swarm optimization driven by evolving elite group (PSO-EEG) algorithm. The hybrid algorithm combines the movement update property of canonical PSO with the evolutionary characteristics of EP. It is processed in two stages; elite group stage by EP and ordinary group stage by PSO. For the former group, a novel concept of evolving elite group (EEG) is introduced, which consists of relatively superior particles in a population. The elite particles are evolved by mutation and selection scheme of EP. The other ordinary particles refer to the closest elite particle as well as the global best and the personal best, to update their location. Simulation results demonstrate the proposed PSO-EEG is highly competitive in terms of robustness, accuracy and convergence speed for five well-known complex test functions.