Collaborative Optimization Algorithm Based on the Penalty Function

Zhi Xia Jiang; Pin Chao Meng; Yan Zhong Li; Wei Shi Yin

doi:10.4028/www.scientific.net/AMM.538.447

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 538Collaborative Optimization Algorithm Based on the...

Collaborative Optimization Algorithm Based on the Penalty Function

Abstract:

The paper discusses the collaborative optimization problems with bounded. Based the penalty function the system-level optimization convert to a unconstraint programming. To the discipline-level optimization, the normalized weighted coefficients are used and combine relaxation factors to solve. It uses the relaxation factor to expand the feasible region, and possibly makes the iteration in the calculation process run inside feasible region. The data have shown that the algorithm has expanded the choice range of the initial points with high calculation accuracy and better algorithm stability.

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Periodical:

Applied Mechanics and Materials (Volume 538)

Pages:

447-450

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.538.447

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Online since:

April 2014

Authors:

Zhi Xia Jiang*, Pin Chao Meng, Yan Zhong Li, Wei Shi Yin

Keywords:

Collaborative Optimization, Penalty Function, Relaxation Factor

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* - Corresponding Author

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