Satisficing Approximation Response Model Based on Neural Network in Multidisciplinary Collaborative Optimization

  • Authors:

  • Ye Tao;Hong-Zhong Huang;Bao-Gui Wu

  • Affiliations:

  • Key Laboratory for Dalian University of Technology Precision & Non-traditional, Machining of Ministry of Education, Dalian, Liaoning 116023, China and School of Information Eng., Dalian Fisheries ...;School of Mechatronics Eng., University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, China;Key Laboratory for Dalian University of Technology Precision & Non-traditional, Machining of Ministry of Education, Dalian, Liaoning 116023, China

  • Venue:
  • ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
  • Year:
  • 2007

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Abstract

Collaborative optimization (CO), one of the multidisciplinary design optimization (MDO) approaches, is a two-level optimization method for large-scale and distributed-analysis engineering design problem. In practical application, CO exists some known weaknesses, such as slow convergence, complex numerical computation, which result in further difficulties when modeling the satisfaction degree in CO. This paper proposes the use of approximation response model in place of discipline-level optimization in order to relieve the aforementioned difficulties. In addition, a satisficing back propagation neural network based on multiple-quality and multiple-satisfaction mapping criterion is applied to the design of the satisfaction degree approximation for disciplinary objective. An example of electronic packaging problem is provided to demonstrate the feasibility of the proposed method.