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Optimization Algorithms as Tools for the Solution of Inverse Problems

This paper presents the main capabilities of IOSO (Indirect Optimization based on Self-Organization) technology algorithms, which can be used for the optimization of objective functions resulting from certain formulations of inverse problems. IOSO implements a novel evolutionary response surface strategy. This strategy differs significantly from both the traditional approaches of nonlinear programming and the traditional response surface methodology. Because of that, IOSO algorithms have higher efficiency, provide wider range of capabilities, and are practically insensitive with respect to the types of objective function and constraints. They could be smooth, non-differentiable, stochastic, with multiple optima, with the portions of the design space where objective function and constraints could not be evaluated at all, with the objective function and constraints dependent on mixed variables, etc. The capabilities of IOSO software are demonstrated using examples of solving complex multidimensional (up to 200 design variables) problems. The examples employ technology of multilevel, multiobjective, and parallel optimization. Igor N. Egorov, Gennadiy V. Kretinin and Igor A. Leshchenko