Evolutionary methods for multidisciplinary optimization applied to the design of Unmanned Air Vehicles systems

The implementation and use of a framework in which engineering optimization problems can be analysed are described. In the first part, the foundations of the framework and the hierarchical asynchronous parallel multi-objective evolutionary algorithms (HAPMOEAs) are presented. These are based upon evolution strategies and incorporate the concepts of multi-objective optimization, hierarchical topology, asynchronous evaluation of candidate solutions, and parallel computing. The methodology is presented first and the potential of HAPMOEAs for solving multi-criteria optimization problems is demonstrated on test case problems of increasing difficulty. In the second part of the article several recent applications of multi-objective and multidisciplinary optimization are described. These illustrate the capabilities of the framework and methodology for the design of Unmanned Air Vehicles and Unmanned Combat Air Vehicles systems. The application presented deals with a two-objective (drag and weight) Unmanned Air Vehicles wing plan-form optimization. The basic concepts are refined and more sophisticated software and design tools with low- and high-fidelity Computational Fluid Dynamics and Finite Element Analysis models are introduced. Various features described in the text are used to meet the challenge in optimization presented by these test cases.