Abstract Solving multi-objective design optimization problems can be computationally expensive, particularly when the original objective and/or constraint functions of the prob- lem are costly to compute. To reduce computational cost, a surrogate model can be constructed that is less costly than the original objective and/or constraint func- tions. The surrogate is then combined with an optimizer to solve the problem. The proposed approach is an online surrogate-based optimization method in which the sur- rogate is built and improved iteratively as the optimizer converges to the solution. The primary contribution of this paper is a new approach based on generative adver- sarial networks, aided by a constraint boundary-informed support vector machine, to predict whether the solutions generated by the approach are feasible or not. The per- formance of the proposed method is compared with that of two other methods from the literature. The comparison is based on several quality metrics and uses numerical and engineering test problems. The approach is also demon- strated with a complex case study for the operation of an unmanned surface vessel. The results indicate that the proposed approach outperforms the other approaches for most of the quality metrics and test problems considered.
Chattejee et al. (Wed,) studied this question.