Space-filling experimental designs for constrained design spaces

Conventional space-filling experimental design provides uniform coverage of a hypercube design space. When constraints are imposed, the results may contain many infeasible points. Simply omitting these points leads to fewer feasible points than desired and a design of experiments that is not optimally distributed. In this research, an adaptive method is developed to create space-filling points in arbitrarily constrained spaces. First, a design space reconstruction method is developed to reduce the invalid exploration space and enhance the efficiency of experimental designs. Then, a synthetic criterion of uniformity and feasibility is proposed and optimized by the enhanced stochastic evolutionary method to obtain the initial sampling combination. Finally, an adaptive adjustment strategy of design levels is constructed to obtain the required number of feasible points. Various test cases with convex and non-convex, connected and non-connected design spaces are implemented to verify the efficacy of the proposed method.