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Main features of IOSO technology in details
At each IOSO iteration the internally constructed response surface model
for the objective is being optimized within the current search region.
This step is followed by a direct call to the actual mathematical model
of the system for the candidate optimal point obtained from optimizing
internal response surface model. During IOSO operation, the information
about the system behavior is stored for the points in the neighborhood
of the extremum, so that the response surface model becomes more accurate
for this search area. The following steps are internally taken while proceeding
from one IOSO iteration to another:
- the modification of the experiment plan;
- the adaptive adjustment of the current search area;
- the function type choice (global or middle-range) for the response
surface model;
- the adjustment of the response surface model;
- the modification of both parameters and structure of the optimization
algorithms;
- if necessary, the selection of the new promising points within the
search area.
IOSO iteration scheme.
Flexible structure of IOSO main strategy provides wide opportunities
for developing the new approaches that can reduce the computational time
for complex real-life optimization problems.
Possible kinds of objectives:
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| smooth; |
non-differentiable; |
stochastic; |
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| multiextremum; |
with the uncomputability regions; |
with mixed variables. |
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