A new improved Quantum-behaved Particle Swarm Optimization model

Abstract

Quantum-behaved particle swarm optimization (QPSO) is a recently developed particle swarm optimization (PSO) algorithm based on quantum-behaved. In this study, a new improved QPSO based on public history researching and variant particle was proposed. On the base of using the better recording locations of all particles and the mutation of the best behaved particle, the particle swarm is filtrated and the convergence speed is accelerated. The testing results indicate that this method improves convergence speed and enhances the global searching ability. The proposed model can be used in the cased of real-time calculation and resources limited.