Quantum-behaved particle swarm optimization with elitist mean best position

Quantum-behaved Particle Swarm Optimization (QPSO) algorithm is a global convergence guaranteed algorithms, which outperforms traditional PSOs in search ability as well as having fewer parameters to control. In this paper, in order to depict the thinking model of people accurately that the decision-making is always influenced by the important part factors which we called elitist, so elitist mean best position is developed in QPSO to balance the global searching ability and convergence speed, and proposes a revised QPSO algorithms (EQPSO). After that, the revised QPSO algorithm is tested on several benchmark functions compared with standard QPSO and the experiment results show its superiority.