Horsetail matching: a flexible approach to optimization under uncertainty
Taylor & Francis
MetadataShow full item record
Cook, L., & Jarrett, J. (2017). Horsetail matching: a flexible approach to optimization under uncertainty. Engineering Optimization https://doi.org/10.1080/0305215X.2017.1327581
It is important to design engineering systems to be robust with respect to uncertainties in the design process. Often, this is done by considering statistical moments, but over-reliance on statistical moments when formulating a robust optimization can produce designs that are stochastically dominated by other feasible designs. This article instead proposes a formulation for optimization under uncertainty that minimizes the difference between a design’s cumulative distribution function and a target. A standard target is proposed that produces stochastically non-dominated designs, but the formulation also offers enough flexibility to recover existing approaches for robust optimization. A numerical implementation is developed that employs kernels to give a differentiable objective function. The method is applied to algebraic test problems and a robust transonic airfoil design problem where it is compared to multi-objective, weighted-sum and density matching approaches to robust optimization; several advantages over these existing methods are demonstrated.
optimization under uncertainty, horsetail matching, robust optimization, density matching, probabilistic methods
Is supplemented by: https://doi.org/10.17863/CAM.9695
This work was supported by the UK Engineering and Physical Sciences Research Council (EPSRC) under grant number EP/L504920/1.
Embargo Lift Date
External DOI: https://doi.org/10.1080/0305215X.2017.1327581
This record's URL: https://www.repository.cam.ac.uk/handle/1810/264035