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传统的再入轨迹优化设计通常只考虑单目标优化问题,例如最小热流、最小大航程、最小控制能量等。随着人们对降低费用和提高性能的期望越来越高,多目标再入轨迹优化问题也引起了注意。以往人们通过加权因子等方法将多目标问题转化为单目标问题,避免了复杂的多目标优化算法的应用。但也引入了新的参数,且每次优化只能获得与该参数相关的1个解。N SGA-II算法是最近发展起来的具有优良性能的多目标遗传算法,它引入了快速分类、约束支配和精英策略,1次运行可以获得多个Pareto最优解。文中利用N SGA-II算法来求解具有最小热载和最大横程的2个目标的再入轨迹优化问题。算例表明N SGA-II算法能够有效地搜索到优化轨迹的Pareto前沿,是RLV初步设计的有力工具。
Traditional re-entry trajectory optimization usually only consider single-objective optimization problems, such as minimum heat flow, minimum long range, minimum control energy and so on. As people expect more and more to reduce costs and improve performance, multi-target re-entry trajectory optimization issues also attracted attention. In the past, people changed the multi-objective problem into the single-goal problem by means of weighting factors and avoided the application of complex multi-objective optimization algorithms. However, new parameters have also been introduced and only one solution related to this parameter can be obtained for each optimization. N SGA-II algorithm is a recently developed multi-objective genetic algorithm with excellent performance. It introduces rapid classification, constraint dominance and elite strategy, and can obtain multiple Pareto optimal solutions in one run. In this paper, N SGA-II algorithm is used to solve the recursion trajectory optimization problem of two targets with minimum heat load and maximum cross-section. The examples show that the N SGA-II algorithm can effectively search the Pareto front of the optimized trajectory, which is a powerful tool for the initial design of RLV.