Abstract
Abstract This paper develops a method to integrate user knowledge into the optimization process by simultaneously modelling feasible design space and optimizing an objective function. In engineering, feasible design space is a constraint similar to those in optimization problems. However, not all constraints can be explicitly written as mathematical functions. This includes manufacturing concerns, ergonomic issues, complex geometric considerations, or exploring material options for a particular application. There needs to be a way to integrate designer knowledge into the design process and, preferably, use that to guide an optimization problem. In this research, these constraints are modeled using classification surrogate models and incorporated with Bayesian optimization. By suggesting design options to a user and allowing them to box off areas of feasible and infeasible designs, the method models both the feasible design space and an objective function probability of new design targets that are more optimal and have a high probability of being feasible. This proposed method is first proven with test optimization problems to show viability then is extended to include user feedback. This paper shows that by allowing users to box off areas of feasible and infeasible designs, it can effectively guide the optimization process to a feasible solution.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
