User of counterpropagation neural networks to enhance the concurrent subspace optimization strategy

User of counterpropagation neural networks to enhance the concurrent subspace optimization strategy

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Article ID: iaor20021307
Country: Netherlands
Volume: 33
Issue: 3
Start Page Number: 327
End Page Number: 349
Publication Date: Feb 2000
Journal: Engineering Optimization
Authors: ,
Keywords: optimization, neural networks, engineering
Abstract:

The paper explores the use of artificial neural networks in a concurrent optimization strategy that derives from a decomposition based approach to design of large-scale engineering systems. These problems are characterized by complex couplings that render parametric design methods inappropriate as solution tools. Decomposition methods reduce the large dimensionality problem into a sequence of smaller, more tractable optimization problems, each with a smaller set of design variables and constraints. The decomposed subproblems are rarely decoupled completely, and design changes in one subproblem have a profound influence on changes in another subproblem. Essential components of decomposition based design methods are strategies to identify a topology for problem decomposition, and to develop coordination strategies which account for couplings among the decomposed problems. The paper examines the effectiveness of artificial neural networks as a tool to both acount for the coupling, and to develop methods to coordinate the solution in the different subproblems to a converged optimal design.

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