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Abstract An efficient global-local hybrid optimization method is developed combining a genetic algorithm (GA) as a global optimizer and a method of feasible direction (MFD) as a local optimizer. During the global iteration using GA, the design sensitivity information with respect to design variables is used to perform a what-if study for efficient crossover. After the final global iteration, a local optimization can be followed to further improve the solution obtained from GA. The developed global-local hybrid optimization method is applied to simple mathematical problems to compare the efficiency and accuracy of the proposed method with that of the conventional genetic algorithm. The objective function using the proposed method converges to the optimum much faster than the conventional GA. It is also able to find the global optimum very quickly even though the starting point is near a local minimum. The proposed method finds the known solution of the mathematical problems very quickly whereas the conventional GA sometimes fails to find the exact solution even though it requires more iteration. The application of the developed method is also extended to the practical engineering problems, which shows very good and efficient results.
Publication Date 2000-10-26

Kyu-Yeul Lee, Seon-Ho Cho, Myung-Il Roh, "An Efficient Global-Local Hybrid Optimization Method Using Design Sensitivity Analysis", Proceedings of OptiCon 2000, Newport Beach, USA, pp. 1-13, 2000.10.26-27


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