Nosachev

Modern methods for optimization investigation of complex systems are based on development and updating the systems’ mathematical models in connection with solving the corresponding inverse problems. The optimization approach is one of the main approaches to solving the inverse problems. In the main case it is necessary to find a global extremum of not everywhere differentiable criterion function. When the number of variables is large they use the stochastic global optimization algorithms. As stochastic algorithms yield too expensive solutions, so this drawback restricts their applications. Developing hybrid algorithms that combine a stochastic algorithm for scanning the variable space with deterministic local search method is a promising way. A new hybrid algorithm that integrates a multiple Metropolis algorithm and the Hooke–Jeeves method for the local search is proposed. Some results on solving the global optimization benchmark are presented.