Title: Simulated polyhedral clouds in robust optimisation
Authors: Martin Fuchs
Addresses: CERFACS, Parallel Algorithms Team, 42 Avenue Gaspard Coriolis, Toulouse 31057, France
Abstract: Past studies of uncertainty handling with polyhedral clouds have already shown strength in dealing with higher dimensional uncertainties in robust optimisation, even in case of partial ignorance of statistical information. However, the number of function evaluations necessary to quantify and propagate the uncertainties has been too high to be useful in many real-life applications with respect to limitations of computational cost. In this paper, we propose a simulation-based approach for optimisation over a polyhedron, inspired by the Cauchy deviates method. Thus, we achieve a computationally efficient method to compute worst-case scenarios with polyhedral clouds which we embed in a robust optimisation problem formulation. We apply the method to two test cases from space system design.
Keywords: polyhedral clouds; robust optimisation; high-dimensional uncertainty handling; Cauchy deviates method; incomplete information; reliable computing; simulation; polyhedron; space system design.
International Journal of Reliability and Safety, 2012 Vol.6 No.1/2/3, pp.65 - 81
Received: 28 May 2010
Accepted: 28 Apr 2011
Published online: 27 Dec 2014 *