A Generic Framework for the Design Optimisation of Multidisciplinary UAV Intelligent Systems using Evolutionary Computing

Abstract

This paper describes the formulation and application of a design framework that supports the complex task of multidisciplinary design optimisation of Unmanned Aerial Vehicles (UAVs). The framework includes a Graphical User Interface (GUI), a robust Evolutionary Algorithm optimiser, several design modules, mesh generators and post-processing capabilities in an integrated platform. Traditional deterministic optimisation techniques for MDO are effective when applied to specific problems and within a specified range. A new class of optimisation techniques named Hierarchical Asynchronous Parallel Evolutionary Algorithms (HAPEAs) have shown to be robust as they require no derivatives or gradients of the objective function, have the capability of finding globally optimum solutions amongst many local optima, can be executed asynchronously in parallel and adapted easily to arbitrary solver codes without major modifications. The application of the methodology is illustrated on multi-criteria and multidisciplinary design problems. Results indicate the practicality and robustness of the method in finding optimal solutions and Pareto trade-offs between the disciplinary analyses and producing a set of non dominated individuals.