Multidimensional stochastic optimization plays important role in analysis and control of many technical systems. For solving difficult multidimensional optimization problems, it is suggested to use randomized stochastic approximation algorithms with input perturbation. These algorithms have simple form and give reliable estimates of unknown parameters at 'almost arbitrary' interference in observations. The optimal techniques of algorithm parameters selection are substantiated.