关键词: 微粒群优化算法, 交换, 进化模型, Shearlet变换, 峰值信噪比

Abstract: By analyzing the deficiencies of two sub-swarms exchange particle swarm optimization based on different evolutionary models，an improved two sub-swarms exchange particle swarm optimization algorithm is proposed.The algorithm divides the particles into two sub-swarms of the same size，the first sub-swarm using the standard PSO evolutionary model and the second sub-swarm using the Cognition Only evolutionary model.When the particles evolve to a stable state，part of the particles from the first sub-swarm are randomly selected to exchange with particles with the worst fitness values from the second sub-swarm.The above-mentioned operations are repeated until the optimal solution is found.Experiment results show that the proposed algorithm has better global search capability and optimal convergence rate.To verify the practicality of the algorithm，the improved algorithm is applied to Shearlet image denoising.According to the distribution characteristics of Shearlet transform domain coefficients of different scales and directions，the method uses the improved algorithm to adaptively determine the optimal thresholds of different scales and directions，to achieve image content-based adaptive denoising.Experiment results show that the method can effectively filter out image noise and better retain edge information，and the denoised images have higher Peak Signal to Noise Ratio（PSNR）.

Key words: particle swarm optimization, exchange, evolutionary model, Shearlet transform, Peak Signal to Noise Ratio（PSNR）