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Conceptual Design Of Uav Using Kriging Based Multi-objective Genetic Algorithm

机译：基于克里格多目标遗传算法的无人机概念设计

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This paper highlights unmanned aerial vehicle (UAV) conceptual design using the multi-objective genetic algorithm (MOGA). The design problem is formulated as a multidisciplinary design optimisation (MDO) problem by coupling aerodynamic and structural analysis. The UAV considered in this paper is a low speed, long endurance aircraft. The optimisation problem uses endurance maximization and wing weight minimisation as dual objective functions. In this multi-objective optimisation, aspect ratio, wing loading, taper ratio, thickness-to-chord ratio, loiter velocity and loiter altitude are considered as design variables with stall speed, maximum speed and rate of climb as constraints. The MDO system integrates the aircraft design code, RDS and an empirical relation for objective function evaluation. In this study, the optimisation problem is solved in two approaches. In the first approach, the RDS code is directly integrated in the optimisation loop. In the second approach, Kriging model is employed. The second approach is fast and efficient as the meta-model reduces the time of computation. A relatively new multi-objective evolutionary algorithm named NSGA-II (non-dominated sorting genetic algorithm) is used to capture the full Pareto front for the dual objective problem. As a result of optimisation using multi-objective genetic algorithm, several non-dominated solutions indicating number of useful Pareto optimal designs is identified.
机译：本文重点介绍了使用多目标遗传算法（MOGA）的无人机（UAV）概念设计。通过将空气动力学和结构分析相结合，将设计问题表述为多学科设计优化（MDO）问题。本文中考虑的无人机是一种低速，长寿命的飞机。优化问题将耐力最大化和机翼重量最小化作为双重目标函数。在这种多目标优化中，长宽比，机翼载荷，锥度比，厚度与弦比，游荡速度和游荡高度被视为设计变量，以失速，最大速度和爬升率为约束。 MDO系统集成了飞机设计规范，RDS和用于目标功能评估的经验关系。在本研究中，通过两种方法解决了优化问题。在第一种方法中，RDS代码直接集成在优化循环中。在第二种方法中，采用了克里格模型。第二种方法快速有效，因为元模型减少了计算时间。相对较新的称为NSGA-II的多目标进化算法（非支配排序遗传算法）用于捕获双目标问题的完整Pareto前沿。使用多目标遗传算法进行优化的结果是，确定了一些非支配解，这些解表明了有用的帕累托最优设计的数量。

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