Powerful tool for nonlinear constrained optimization. You have an object, which should be improved by optimum coordination of several independent parameters, and executable mathematical model of this object, which computes its efficiency index depending on these parameters. Using IOSO NS GT 2.0, you can quickly and efficiently solve non-linear parametric optimization problem with dimensionality up to 5 independent variables and 5 functional inequality constraints. Family of IOSO NS versions utilizes set of heuristic self-organizing optimization algorithms. These algorithms are based on usage of owner-developed approximation technology. At every iteration of search for optimum response surfaces are being built for objective and constrained parameters, then these response surfaces are being optimized. Thus, the number of direct calls to mathematical model is minimized, and time expenditures for problem solution are also minimal. Using approximation technology makes it possible to solve successfully problems with complex topology of objective and constraints. Unlike the gradient methods, IOSO NS works robustly with discontinuous, non-differentiatable, noised and locally incomputable objectives and constraints. Software has friendly user interface. It doesnlt require from user to be an expert in a field of numerical optimization. The number of algorithm settings available to be changed by user in IOSO NS program is minimal, all algorithm parameters are being adaptively changed during the process of search for optimum. The only thing required from user is to make a correct statement of optimization problem and prepare mathematical model input and output. IOSO NS has extended help system and samples of optimization projects with their source codes in FORTRAN for making it easier for you to begin work. If you need the optimization tool for larger dimensionalities, you can purchase extended features for IOSO NS GT 2.0 (up to 100 variables and 100 constraints).

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IOSO NS

Powerful tool for nonlinear constrained optimization using your executable mathematical model.

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