A novel quantum-inspired particle swarm algorithm and its application

The quantum-inspired optimization algorithm is a rising intelligence algorithm which merges quantum mechanics and computing intelligence and outperforms original PSO in search ability but has fewer parameters to control. In this paper, an improved quantum-behaved particle swarm optimization algorithm using mutation operator (MQPSO) was improved which aimed at enhancing global search capability. The application of mutation operators diversifies the QPSO population and improves the performance in preventing premature convergence to local minima. The proposed improved QPSO is tested on several benchmark functions and compared with QPSO and standard PSO. The application of QPSO to seismic wave impedance inversion demonstrates the effectiveness and efficiency of the QPSO.