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IOSO algorithms equally well handle optimization problems
with various types of objective function
We conducted extensive tests of estimating IOSO Technology
algorithms efficiency for the following types of objective functions:
- smooth;
- stochastic;
- non-differentiable;
- dependent on mixed variables (continuous and discrete);
- with the region in the design space where the objective function can
not be evaluated.
The figure below shows the results for the test functions collected by
Eric Sandgren (Sandgren, Eric. “The Utility of Nonlinear Programming Algorithms”,
a thesis submitted for the Degree of Doctor of Philosophy, Purdue Univercity,
1977). Each of these functions was considered initially in the original
formulation and then was modified to model the objective functions of
various types.
The obtained results confirm that IOSO Technology algorithms are practically
independent of the objective function types and can be successfully applied
in various science and technology fields.
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| smooth; |
non-differentiable; |
stochastic; |
dependent on mixed variables (continuous
and discrete) |
with the non-calculation region |
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