A Diversity-Guided Quantum-Behaved Particle Swarm Optimization Algorithm

Abstract

One of the primary complaints toward Particle Swarm Optimization (PSO) is the occurrence of premature convergence. Quantum-behaved Particle Swarm Optimization (QPSO), a novel variant of PSO, is a global convergent algorithm whose search strategy makes it own stronger global search ability than PSO. But like PSO and other evolutionary optimization technique, premature convergence in the QPSO is also inevitable and may deteriorate with the problem to be solved becoming more complex. In this paper, we propose a new Diversity-Guided QPSO (DGQPSO), in which a mutation operation is exerted on global best particle to prevent the swarm from clustering, enabling the particle to escape the sub-optima more easily. The DGQPSO, along with the PSO and QPSO, is tested on several benchmark functions for performance comparison. The experiment results show that the DGQPSO outperforms the PSO and QPSO in alleviating the premature convergence.