R. Luus (Canada)
Optimization; Parameter estimation; Model reduction; Region size
The use of a multi-pass procedure with a rela tively small number of randomly chosen points in the LJ optimization procedure yields faster convergence than the use of a very large number of points in a sin gle pass. The efficiency is increased by choosing the region size at the beginning of a pass, over which the randomly chosen candidates for variables are chosen, to be the range over which the variables have changed during the previous pass. To avoid the possibility of region collapse when the change during a pass in some variable is very small, a region collapse parameter is introduced, so that if the calculated region size is less than , then the region size is put equal to at the beginning of the subsequent pass. This parameter has a very significant effect on the convergence rate, espe cially near the optimum. Three optimization problems are used to illustrate the effect of and to test the via bility of using a progressively smaller value for as the number of passes increases.
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