An Improved Quantum Particle Swarm Optimization Algorithm Based on Real Coding Method

This paper proposes a novel optimization algorithm combined the mechanism of quantum evolutionary algorithm and real-coding method, called an improved quantum particle swarm optimization algorithm (IQPSO). Like the traditional particle swarm optimization, IQPSO is also characterized by position vector and velocity vector to implement the evolution process. However, the particle of IQPSO is divided into two parts. The first part is real-valued coding; and the rest of it is quantum probability amplitude. Further, IQPSO uses quantum probability amplitude as velocity vector, and a Q-gate is applied to update the quantum probability amplitude. At the same time, a self adaptable mutation operator is used to improve the diversity of population. To demonstrate the effectiveness of IQPSO, experiments are carried out on the function optimization; and the results show that IQPSO performs well.