LOW-COST STOCHASTIC OPTIMIZATION FOR ENGINEERING APPLICATIONS

This paper presents a technique which when used with Evolutionary Algorithms (Genetic Algorithms, Evolutionary Strategies) reduces noticeably the computational cost by decreasing the number of exact evaluations required to reach the optimal solution. This technique is based on the use of “local” surrogate evaluation models, namely radial basis function networks which are trained and used during the evolution. Two engineering applications, namely the inverse design of an airfoil and the optimization of an optical filter layout, are used to demonstrate the gain offered by the proposed technique.