A Surrogate Genetic Programming Based Model to Facilitate Robust Multi-Objective Optimization: A Case Study in Magnetostatics

A common drawback of robust optimization methods is the effort expended to compute the influence of uncertainties, because the objective and constraint functions must be re-evaluated many times. This disadvantage can be aggravated if time-consuming methods, such as boundary or finite element methods are required to calculate the optimization functions. To overcome this difficulty, we propose the use of genetic programming to obtain high-quality surrogate functions that are quickly evaluated. Such functions can be used to compute the values of the optimization functions in place of the burdensome methods. The proposal has been tested on a version of the TEAM 22 benchmark problem with uncertainties in decision parameters. The performance of the methodology has been compared with results in the literature, ensuring its suitability, significant CPU time savings and substantial reduction in the number of computational simulations.