IOSO software improves an object through coordination of multiple parameters and a mathematical model. Efficiency index is computed for optimization. IOSO is quick and efficient in solving optimization problems.
As someone who has struggled with optimizing objects with multiple independent parameters, I was excited to try the IOSO NS 1.1 Academic edition software. With this program, you have a powerful mathematical model at your disposal that can compute the efficiency index of your object based on its parameters.
The IOSO NS family of software versions utilizes heuristic self-organizing optimization algorithms that are based on an owner-developed approximation technology. This technology enables the construction of response surfaces for objective and constrained parameters so that the number of direct calls to the mathematical model can be minimized. This results in minimal time expenditures for finding the solution to your problem.
One of the most significant advantages of using this software is that it can successfully solve problems with complex topology of objective and constraints. This is not true for gradient methods, which typically struggle with discontinuous, non-differentiatable, noised, or locally incomputable objectives and constraints.
The user interface is friendly and does not require expertise in numerical optimization. There are minimal algorithm settings that need to be adjusted, as all algorithm parameters adaptively change during the search for optimum. The only requirements of the user are to correctly state the optimization problem and prepare the mathematical model input and output.
Lastly, the IOSO NS program has an extended help system and optimization project samples with source codes in FORTRAN, which helps users to begin work immediate. Overall, I highly recommend IOSO NS 1.1 Academic edition to anyone looking to quickly and efficiently solve non-linear optimization problems with dimensionality of up to five independent variables and five functional inequality constraints.
The IOSO NS family of software versions utilizes heuristic self-organizing optimization algorithms that are based on an owner-developed approximation technology. This technology enables the construction of response surfaces for objective and constrained parameters so that the number of direct calls to the mathematical model can be minimized. This results in minimal time expenditures for finding the solution to your problem.
One of the most significant advantages of using this software is that it can successfully solve problems with complex topology of objective and constraints. This is not true for gradient methods, which typically struggle with discontinuous, non-differentiatable, noised, or locally incomputable objectives and constraints.
The user interface is friendly and does not require expertise in numerical optimization. There are minimal algorithm settings that need to be adjusted, as all algorithm parameters adaptively change during the search for optimum. The only requirements of the user are to correctly state the optimization problem and prepare the mathematical model input and output.
Lastly, the IOSO NS program has an extended help system and optimization project samples with source codes in FORTRAN, which helps users to begin work immediate. Overall, I highly recommend IOSO NS 1.1 Academic edition to anyone looking to quickly and efficiently solve non-linear optimization problems with dimensionality of up to five independent variables and five functional inequality constraints.
What's New
Version 1.1: N/A