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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:

smooth; non-differentiable; stochastic;
multiextremum; with the uncomputability regions; with mixed variables.

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Sigma Technology 2001.