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, 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 doesn't
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. These features allow you optimization
with up to 100 independent variables and 100
inequality constraints.

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