Probability model for an adaptive random search algorithm

One class of multi-modal optimization approaches that could overcome the curse of dimensionality is random search optimization. However, very little is known about how the parameters of such algorithms should be adjusted in order to achieve a desired convergence speed. In this paper we extend our previous work of developing the probability model for one-parameter systems to two parameter problems when the search domain is a circle. The analysis reveals the key parameters affecting convergence time and provides insight on ways to tune the algorithm for more rapid convergence.