Homogeneous algorithms of multiextremal optimization for time-consuming objective functions

A number of ways to accelerate homogeneous algorithms for global optimization are proposed. Theorems on the possibilities of acceleration without loss of convergence to the global minimum are proved. Models of objective functions are considered. It is proved that the use of these models ensures a convergence to the global minimum of the objective function. A model to determine the application field of the algorithm is constructed. Some numerical results of testing the proposed algorithm are discussed.