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

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. Nomenclature UAV = unmanned aerial vehicle C p = pressure coefficient C d = drag coefficient C l = drag coefficient t/c = thickness-to-chord ratio Cm = pitching moment coefficient W SC = wing structural weight AR = wing aspect ratio λ br = taper ratio root to break λ bt = taper ratio break to tip Λ = wing 1/4 chord sweep b l = break location