A method to improve computational efficiency for CSSO and BLISS

A considerable portion of the computational cost results from the solution of sensitivity information in concurrent subspace optimization and bi-level integrated system synthesis. A novel method to update sensitivity information is suggested to improve the computational efficiency in this brief note. Firstly, both the approximate insensitive terms and approximate linear terms in sensitivity information are identified, and then their values are temporarily kept constant for multiple multidisciplinary design optimization cycles. A practical engineering case study is presented to demonstrate the effectiveness of the proposed method.

[1]  T Haftka Raphael,et al.  Multidisciplinary aerospace design optimization: survey of recent developments , 1996 .

[2]  John E. Renaud,et al.  Multiobjective Collaborative Optimization , 1997 .

[3]  F. Jose,et al.  Convergence of Trust Region Augmented Lagrangian Methods Using Variable Fidelity Approximation Data , 1997 .

[4]  Bernard Grossman,et al.  Polynomial Response Surface Approximations for the Multidisciplinary Design Optimization of a High Speed Civil Transport , 2001 .

[5]  J. Renaud,et al.  Approximation in nonhierarchic system optimization , 1994 .

[6]  J. Renaud,et al.  Trust region model management in multidisciplinary design optimization , 2000 .

[7]  Christina Bloebaum,et al.  Development of multiple cycle coupling suspension in the optimization of complex systems , 2001 .

[8]  C. McCulley,et al.  COMPARISON OF HEURISTIC CONVERGENCE STRATEGIES FOR MULTIDISCIPLINARY ANALYSIS , 1998 .

[9]  Jaroslaw Sobieszczanski-Sobieski,et al.  MDO can help resolve the designer's dilemma. [multidisciplinary design optimization] , 1991 .

[10]  Christina Bloebaum,et al.  Coupling strength-based system reduction for complex engineering design , 1995 .

[11]  Marc A. Stelmack,et al.  Neural network approximation of mixed continuous/discrete systems in multidiscplinary design , 1998 .

[12]  T Watson Layne,et al.  Multidisciplinary Optimization of a Supersonic Transport Using Design of Experiments Theory and Response Surface Modeling , 1997 .

[13]  Christina Bloebaum,et al.  A genetic tool for optimal design sequencing in complex engineering systems , 1996 .

[14]  Farrokh Mistree,et al.  Kriging Models for Global Approximation in Simulation-Based Multidisciplinary Design Optimization , 2001 .

[15]  Robert D. Braun,et al.  Collaborative optimization: an architecture for large-scale distributed design , 1996 .

[16]  Christina Bloebaum,et al.  Optimal sequencing for complex engineering systems using genetic algorithms , 1994 .

[17]  Jaroslaw Sobieszczanski-Sobieski,et al.  Multidisciplinary aerospace design optimization - Survey of recent developments , 1996 .

[18]  J. Sobieszczanski-Sobieski,et al.  Bilevel Integrated System Synthesis for Concurrent and Distributed Processing , 2002 .

[19]  Surya N. Patnaik,et al.  Subproblem optimization with regression and neural network approximators , 2005 .

[20]  Marc A. Stelmack,et al.  AIAA 98-0916 Neural Network Approximation of Mixed Continuous/Discrete Systems in Multidisciplinary Design , 1998 .

[21]  James L. Rogers DeMAID: A Design Manager's Aide for Intelligent Decomposition user's guide , 1989 .

[22]  J. F. Rodríguez,et al.  Sequential approximate optimization using variable fidelity response surface approximations , 2000 .

[23]  Jeremy S. Agte,et al.  Bi-Level Integrated System Synthesis , 1998 .