An Improved PSO Algorithm Based on SA and Quantum Theory and Its Application

Due to the low computational precision, local optimal solution and slow convergence speed of particle swarm optimization (PSO) algorithm, an improved PSO (SAQPSO) algorithm based on simulated annealing (SA) and quantum theory is proposed in this paper. The first, quantum theory is used to change the updating mode of the particles in order to improve the search speed and the convergence precision, and guarantee the simplification and effectiveness. Then the SA with probability and local search ability is introduced into quantum PSO (QPSO) in order to keep the diversity of the population, avoid falling into local optimum and enhance the global search ability. The SAQPSO algorithm keeps the characteristics of the simple and easy implementation, improves the global optimization ability and the convergence speed and the accuracy. Finally, some benchmark functions are used to prove the validity of the proposed SAQPSO algorithm. The computational results show that the proposed SAQPSO algorithm takes on the fast convergence speed, the better robustness and global search ability.