NEW ADAPTIVE MOVE-LIMIT MANAGEMENT STRATEGY FOR APPROXIMATE OPTIMIZATION, PART 1

Approximations play an important role in multidisciplinary design opti mization by offering system behavior information at a relatively low c ost. Most approximate optimization strategies are sequential, in which an optimization of an approximate problem subject to design variable move limits is iteratively repeated until convergence. The move limits are imposed to restrict the optimization to regions of the design spa ce in which the approximations provide meaningful information. To ensu re convergence of the sequence of approximate optimizations to a Karus h-Kuhn-Tucker solution, a move-limit management strategy is required. Issues of move-limit management are reviewed and a new adaptive strate gy for move-limit management is developed. With its basis in the prova bly convergent trust region methodology, the trust region ratio approx imation method (TRAM) strategy utilizes available gradient information and employs a backtracking process using various two-point approximat ion techniques to provide a flexible move-limit adjustment factor. In a companion paper, the new strategy is tested in comparison to several existing strategies using a variety of multidisciplinary design optim ization test problems. Those implementation studies highlight the abil ity of the TRAM strategy to control the amount of approximation error and efficiently manage the convergence to a Karush-Kuhn-Tucker solutio n.