A Comparison of Global Optimization Methods for the Design of a High-speed Civil Transport

  • Authors:

  • Steven E. Cox;Raphael T. Haftka;Chuck A. Baker;Bernard Grossman;William H. Mason;Layne T. Watson

  • Affiliations:

  • Department of Aerospace Engineering, Mechanics &/ Engineering Science, P.O. Box 116250, University of Florida, Gainesville, FL 32611-6250, USA/;Department of Aerospace Engineering, Mechanics &/ Engineering Science, P.O. Box 116250, University of Florida, Gainesville, FL 32611-6250, USA/;Department of Aerospace and Ocean Engineering, Virginia Polytechnic Institute and State University, 215 Randolph Hall, Mail Stop 0203, Blacksburg, VA 24061, USA/ Current address: Engineous Softwar ...;Department of Aerospace and Ocean Engineering, Virginia Polytechnic Institute and State University, 215 Randolph Hall, Mail Stop 0203, Blacksburg, VA 24061, USA/;Department of Aerospace and Ocean Engineering, Virginia Polytechnic Institute and State University, 215 Randolph Hall, Mail Stop 0203, Blacksburg, VA 24061, USA/;Departments of Computer Science &/ Mathmatics, Virginia Polytechnic Institute and State University, 630 McBryde Hall, Mail Stop 0106, Blacksburg, VA 24061, USA/

  • Venue:
  • Journal of Global Optimization
  • Year:
  • 2001

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Abstract

The conceptual design of aircraft often entails a large number of nonlinear constraints that result in a nonconvex feasible design space and multiple local optima. The design of the high-speed civil transport (HSCT) is used as an example of a highly complex conceptual design with 26 design variables and 68 constraints. This paper compares three global optimization techniques on the HSCT problem and two test problems containing thousands of local optima and noise: multistart local optimizations using either sequential quadratic programming (SQP) as implemented in the design optimization tools (DOT) program or Snyman's dynamic search method, and a modified form of Jones' DIRECT global optimization algorithm. SQP is a local optimizer, while Snyman's algorithm is capable of moving through shallow local minima. The modified DIRECT algorithm is a global search method based on Lipschitzian optimization that locates small promising regions of design space and then uses a local optimizer to converge to the optimum. DOT and the dynamic search algorithms proved to be superior for finding a single optimum masked by noise of trigonometric form. The modified DIRECT algorithm was found to be better for locating the global optimum of functions with many widely separated true local optima.