A Robust Evolutionary Technique For Coupled And Multidisciplinary Design Optimisation Problems In Aeronautics

This paper reviews recent progress made in Evolutionary Algorithms (EAs) for single, multiobjective and Multidisciplinary Design Optimisation (MDO) problems. Specifically we discuss the integration and implementation of a Hierarchical Asynchronous Evolutionary Algorithm (HAPEA) to solve complex engineering problems which can be multi-modal, involve non-linear approximations that are non-differentiable or involve multiple objectives. The algorithm is based upon traditional evolution strategies with the incorporation of an asynchronous function evaluation for the solution. The algorithm is adaptable for multiple population of EAs with variable fidelity models and use the concepts of Game Theory to handle multi-objective problems. Initially we give some examples of the performance of the algorithm for representative single and multi-objective analytical test functions, which involve multiple local minima, discontinuous Pareto fronts or constraints and then two cases related to aircraft design are analyzed. Result indicate that the method is robust and efficient on its application for real world problems