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This document discusses global optimization algorithms that can be used to solve problems with multi-extremum objective functions, such as model calibration problems. It provides an overview of various global optimization approaches, including set covering techniques, random search methods, evolutionary algorithms like genetic algorithms, and methods that perform multiple local searches using clustering. The document then compares the performance of nine global optimization algorithms, including genetic alg

Author

Oktafian Prabandaru

Language

EN