NOTE ON THE EFFECTIVENESS OF STOCHASTIC OPTIMIZATION ALGORITHMS FOR ROBUST DESIGN

Abstract: Robust design optimization (RDO) uses statistical de-cision theory and optimization techniques to optimize a design over a range of uncertainty (introduced by the manufacturing pro-cess and unintended uses). Since engineering objective functions tend to be costly to evaluate and prohibitively expensive to inte-grate (required within RDO), surrogates are introduced to allow the use of traditional optimization methods to find solutions. This paper explores the suitability of radically different (deterministic and stochastic) optimization methods to solve prototypical robust design problems. The algorithms include a genetic algorithm us-ing a penalty function formulation, the simultaneous perturbation 1 stochastic approximation (SPSA) method, and two gradient-based constrained nonlinear optimizers (method of feasible directions and sequential quadratic programming). The results show that the fully deterministic standard optimization algorithms are con-sistently more accurate, consistently more likely to terminate at feasible points, and consistently considerably less expensive than the fully nondeterministic algorithms