This paper describes the theory and practical application of Hierarchical Asynchronous Parallel Multi-objective Evolutionary Algorithms (HAPMOEA) for mission optimisation of Unmanned Aerial Systems (UAS). Optimisation has emerged as a new discipline for UAS in recent years and most of the optimisation efforts are focused on the use of gradient-based techniques. One drawback of these methods is that they are mostly suitable when there is only one objective to be met with or when the objectives are differentiable. A real design or simulation will have more than one objective such as minimising fuel consumption, drag or time to complete the mission. It is usually the case that the problem is highly non-linear and non-differentiable. New techniques are required, and one of such techniques, even though computationally more intensive than gradient-based methods, are Evolutionary Algorithms (EAs). This paper describes an advanced EA methodology and its coupling with simulation analysis tools. Results will indicate the practicality and robustness of the method in finding optimal solutions and Pareto trade-offs between fuel consumption and time to complete the mission of a hybrid UAS by producing a set of non-dominated trajectories and mission from which the designer can choose.
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