Preprint Article Version 1 This version is not peer-reviewed

An Improved Collaborative Algorithm with Artificial Neural Network in Multidisciplinary Design Optimization of AUV

Version 1 : Received: 4 June 2017 / Approved: 5 June 2017 / Online: 5 June 2017 (05:27:31 CEST)

How to cite: Xu, X.; Li, H. An Improved Collaborative Algorithm with Artificial Neural Network in Multidisciplinary Design Optimization of AUV. Preprints 2017, 2017060024 (doi: 10.20944/preprints201706.0024.v1). Xu, X.; Li, H. An Improved Collaborative Algorithm with Artificial Neural Network in Multidisciplinary Design Optimization of AUV. Preprints 2017, 2017060024 (doi: 10.20944/preprints201706.0024.v1).

Abstract

Multidisciplinary Design Optimization (MDO) is the most active field in the design of current complex system engineering, which is possessed with such two difficulties as subsystem information exchange and analytical and computational complexity of systems. Therefore, an improved collaborative optimization algorithm based on ANN (artificial neural network) response surface was proposed dependent on the consistency constraint algorithm and concurrent subspace algorithm. As an optimization method with secondary structure, it satisfied only local constraints in discipline layer, but provided a coordinated mechanism for interdisciplinary conflict in system layer. Finally, it was applied in the multidisciplinary design optimization of autonomous underwater vehicle (AUV). As shown from the result, the MDO convergence stability and reliability of low resistance, low noise and high maneuvering performance of the AUV shape can be ensured by the improved collaborative optimization algorithm, thus verifying the effectiveness of the algorithm.

Subject Areas

collaborative optimization algorithm; artificial neural network (ANN); low noise; low resistance; maneuvering performance

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