Handling simulation failures of a computationally expensive multiobjective optimization problem in pump design

Abstract

Solving real-world optimization problems in engineering and design involves various practical challenges. They include simultaneously optimizing multiple conflicting objective functions that may involve computationally expensive simulations. Failed simulations introduce another practical challenge, as it is not always possible to set constraints a priori to avoid failed simulations. Failed simulations are typically ignored during optimization, which leads to wasting computation resources. When the optimization problem has multiple objective functions, failed simulations can also be misleading for the decision maker while choosing the most preferred solution. Utilizing data collected from previous simulations and enabling the optimization algorithm to avoid failed simulations can reduce the computational requirements. We consider data-driven multiobjective optimization of the diffusor of an axial pump and propose an approach to reduce the number of solutions that fail in expensive computational fluid dynamics simulations. The proposed approach utilizes Kriging surrogate models to approximate the objective functions and is inexpensive to evaluate. We utilize a probabilistic selection approach with constraints in a multiobjective evolutionary algorithm to find solutions with better objective function values, lower uncertainty, and lower probability of failing. Finally, a domain expert chooses the most preferred solution using one’s preferences. Numerical tests show significant improvement in the ratio of feasible solutions to all the available solutions without special treatment of failed simulations. The solutions also have a higher quality (hypervolume) and accuracy than the other tested approaches. The proposed approach provides an efficient way of reducing the number of failed simulations and utilizing offline data in multiobjective design optimization.