Generic parameter penalty architecture

A new architecture for Multidisciplinary Design Optimization, called Generic Parameter Penalty Architecture, is introduced. This architecture can be configured through three parameters that manage the distribution of objective functions and consistencies between main and sub-level optimizations. The parameters can adopt values between zero and one. Five different configurations of these parameters were tested on three problems (an analytical problem, Golinski’s speed reducer, and the combustion of propane). All three parameters of four of these configurations have extreme values (either zero or one), and the fifth one has intermediate values. These values make some of configurations manage the main level and sub-level optimizations in a similar manner to that of All at Once, Collaborative Optimization, and Analytical Target Cascading, which were studied for benchmarking purposes. The convergence and relative error of the solutions obtained by the new architecture were studied and compared to those of the previously stated widespread architectures. Results show that the performance of the new architecture depends on the values of its three parameters. It adopted behaviors similar to those of the reference architectures. Finally, its convergence and relative error, in contrast to the reference architectures, increased with the complexity of the problem.