This paper describes a thorough comparison of ten different search techniques applied to a wing-box design optimisation problem. The techniques used vary from deterministic gradient descent to stochastic Simulated Annealing (SA) and Genetic Algorithms (GAs). The stochastic techniques produced as good solutions as the best found by the deterministic techniques. However, only the stochastic techniques consistently produced very good solutions every run. Significantly, only a distributed genetic algorithm (DGA) and hybrid methods (SA with gradient descent, DGA with gradient descent) had a reliable fast decent to good regions of solution space. Of these the hybrid DGA was significantly better than anything else. The issue of generating solutions stable to perturbations of the problem variables, without greatly increasing the runtime of the objective function, is also discussed. We describe a method for producing highly stable solutions with the DGA while increasing the run time of the objective function by a factor of only 4. No explicit term dealing with stability was added to the objective function.
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