A Fusion-Based Multi-Information Source Optimization Approach using Knowledge Gradient Policies
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© 2018 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved. Design decisions for complex systems often can be made or informed by a variety of information sources. When optimizing such a system, the evaluation of a quantity of interest is typically required at many different input configurations. For systems with expensive to evaluate available information sources, the optimization task can potentially be computationally prohibitive using traditional techniques. This paper presents an information economic approach to the constrained optimization of a system with several available information sources. The approach ensures that all available information is exploited efficiently by fusing newly acquired information with that previously evaluated, and by taking information source query costs into account. Constraints are accounted for via a dynamic penalty method, which allows for exploration of infeasible regions early in the decision process. The approach is demonstrated on a one-dimensional example test problem and an aerodynamic design problem.
author list (cited authors)
Ghoreishi, S. F., & Allaire, D. L.