Comparison of several stochastic parallel optimization control algorithms for adaptive optics system

Optimizing the system performance metric directly is an important method for correcting wave-front distortions in adaptive optics (AO) systems. Appropriate stochastic parallel optimization control algorithm is the key to correcting distorted wave front successfully. Based on several stochastic parallel optimization control algorithms, an adaptive optics system with a 32-element deformable mirror was simulated. Genetic algorithm (GA), the unilateral perturbation stochastic parallel gradient descent (SPGD), the bilateral perturbation SPGD and simulated annealing (SA) were compared in convergence speed, correction capability and local maximum. The results show that because of the unacceptable convergence speed, GA is not suitable for the control of real-time AO system; the bilateral perturbation SPGD is better than the unilateral perturbation SPGD in convergence rate, correction effect and adaptability to different perturbations; SA almost converges nearby the global maximum at probability one and is the fastest algorithm on convergence speed in several algorithms.