A comparison of global optimization methods for the design of a high-speedcivil transport

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 a s an example of a highly complex conceptual design with 26 design variables and 68 constraints. This paper compares three global optimization techniqu es on the HSCT problem and two test problems containing thousands of local optima and noise: multistart local optimizations using either sequential qu adratic programming (SQP) as implemented in the design optimization tools ( DOT) program or Snyman's dynamic search method, and a modified form of Jone s' DIRECT global optimization algorithm. SQP is a local optimizer, while Sn yman's algorithm is capable of moving through shallow local minima. The mod ified DIRECT algorithm is a global search method based on Lipschitzian opti mization that locates small promising regions of design space and then uses a local optimizer to converge to the optimum. DOT and the dynamic search a lgorithms proved to be superior for finding a single optimum masked by nois e of trigonometric form. The modified DIRECT algorithm was found to be bett er for locating the global optimum of functions with many widely separated true local optima.