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- W2191690168 abstract "Multidisciplinary Design Optimization (MDO) is the most active fields in the current complex engineering sys- tem design. Forcing to the defects of traditional Collaborative Optimization, such as unable to convergence or falling into local optimum, we propose a Collaborate Optimization based on Simulated Annealing and Artificial Neural Networks, (SA-ANN-CO). The SA-ANN-CO algorithm inherit the parallel distribution strategy of tradition Collaborative Optimiza- tion, and then establish accuracy Artificial Neural Networks models by Latin Hypercube Experimental design replace the realistic model of sub-disciplines to reduce computing costs and smooth numerical noise. The possibility of falling into local solutions is reduced by using the Simulated Annealing algorithm in system-level. Two classic test examples results show that, SA-ANN-CO algorithm has good robustness and can quickly and effectively converge to the global optimum solution, which provides a effective way for complex engineering systems design." @default.
- W2191690168 created "2016-06-24" @default.
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- W2191690168 date "2015-10-19" @default.
- W2191690168 modified "2023-10-17" @default.
- W2191690168 title "Improvement Multidisciplinary Collaborate Optimization based on Simulated Annealing and Artificial Neural Networks" @default.
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- W2191690168 doi "https://doi.org/10.2174/1874110x01509012306" @default.
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