Abstract: A particle swarm optimization algorithm based on Morlet wavelet mutation was presented to overcome the problems of low convergence precision and easily falling into local extremum. Morlet mutation operation was implemented for each dimension of global extremum，the mutation results were used as new positions of particles, which was selected in certain probability. This strategy made full use of the advantage information of global extremum to guide the particle to approach the optimal solution quickly. At the same time, the fine tuning feature of wavelet function helped the particle jumping out of the local extremum. The simulation experiments on 12 classical test functions showed that the improved algorithm had better performance than SPSO, CLPSO, DEOPSO and HPSOWM algorithms and was suitable for solving function optimization problems.

Key words: particle swarm optimization algorithm, Morlet wavelet, convergence speed, convergence precision；time complexity