Bayesian optimization of variable-size design space problems
This addresses complex system design problems for engineers, but it is incremental as it builds on existing Bayesian Optimization techniques.
The paper tackles optimization problems with variable-size design spaces, where the number and type of variables and constraints change dynamically, by proposing two Bayesian Optimization approaches: a budget allocation strategy and a kernel-based method. Results on test-cases show faster and more consistent convergence compared to standard methods.
Within the framework of complex system design, it is often necessary to solve mixed variable optimization problems, in which the objective and constraint functions can depend simultaneously on continuous and discrete variables. Additionally, complex system design problems occasionally present a variable-size design space. This results in an optimization problem for which the search space varies dynamically (with respect to both number and type of variables) along the optimization process as a function of the values of specific discrete decision variables. Similarly, the number and type of constraints can vary as well. In this paper, two alternative Bayesian Optimization-based approaches are proposed in order to solve this type of optimization problems. The first one consists in a budget allocation strategy allowing to focus the computational budget on the most promising design sub-spaces. The second approach, instead, is based on the definition of a kernel function allowing to compute the covariance between samples characterized by partially different sets of variables. The results obtained on analytical and engineering related test-cases show a faster and more consistent convergence of both proposed methods with respect to the standard approaches.