Quantum-Behaved Particle Swarm Optimization with Diversity-Maintained

Abstract

Quantum-behaved particle swarm optimization (QPSO) algorithm is a global-convergence-guaranteed algorithm, which outperforms original PSO in search ability but has fewer parameters to control. But QPSO algorithm is to be easily trapped into local optima as a result of the rapid decline in diversity. So this paper describes diversity-maintained into QPSO (QPSO-DM) to enhance the diversity of particle swarm and then improve the search ability of QPSO. The experiment results on benchmark functions show that QPSO-DM has stronger global search ability than QPSO and standard PSO.