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: | Hajela P., Arslan M.A. |
Keywords: | optimization, neural networks, engineering |
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.