Algorithm of Combined Simplex Search with State Recognition
Searching optimization is usual for objects with unknown or unsolvable mathematical model, and result depends on the efficiency of algorithm. Simplex search has shown good results when optimization object and measurement results are affected by high level noise. Improvement and investigation of simplex search algorithms is an aim of this research. Characteristics of the object can vary in time during optimization process, and the search has to follow the extreme. If the variation is a result of some measurable but noncontrollable parameters, the search can be improved using the information about this variation in past steps and calculating the prognosis, or performing repeated measurements of these parameters and aim value. The influence of multidimensional measurable parameters is estimated in presented algorithm of combined simplex search with state recognition, and characteristics of the optimization process are investigated. Ill. 6, bibl. 6 (in English; summaries in Lithuanian, English, Russian).
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