Subproblem Optimization with Regression and Neural Network Approximators

摘要

Design optimization of large systems can be attempted through a subproblem strategy. In this strategy, the original problem is divided into a number of smaller problems that are clustered together to obtain a sequence of subproblems. Solution to the large problem is attempted iteratively through repeated solutions to the modest subproblems. This strategy is applicable to structures and to multidisciplinary systems. For structures, clustering the substructures generates the sequence of subproblems. For a multidisciplinary system, individual disciplines, accounting for coupling, can be considered as subproblems. A subproblem, if required, can be further broken down to accommodate subdisciplines. The subproblem strategy is being implemented into the NASA design optimization test bed, referred to as CometBoards. Neural network and regression approximators are employed for reanalysis and sensitivity analysis calculations at the subproblem level. The strategy has been implemented in sequential as well as parallel computational environments. This strategy, which attempts to alleviate algorithmic and reanalysis deficiencies, has the potential to become a powerful design tool. However, several issues have to be addressed before its full potential can be harnessed. This paper illustrates the strategy and addresses some issues.