A PARALLEL GLOBAL OPTIMIZATION ALGORITHM FOR SOLVING INVERSE PROBLEMS
Henryk Telega · 2002
In this paper two improved versions of Genetic Clustering (GC) algorithm [1] are described. GC is a parallel global optimization algorithm that was designed in order to solve such parameter inverse problems in which an approximation of certain level sets (central parts of basins of attractions of local minimizers) is required. The approximation of these sets can be useful when some additional criteria of optimization are considered after main results of parameter identification are obtained. In spite of some good properties of GC, tests have shown that GC is not effective for problems with more than 4 dimensions.