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混合遗传算法及其在运载火箭最优上升轨道设计中的应用
引用本文:罗亚中,唐国金,周黎妮.混合遗传算法及其在运载火箭最优上升轨道设计中的应用[J].国防科技大学学报,2004,26(2):5-8.
作者姓名:罗亚中  唐国金  周黎妮
作者单位:国防科技大学航天与材料工程学院,湖南,长沙410073;国防科技大学航天与材料工程学院,湖南,长沙410073;国防科技大学航天与材料工程学院,湖南,长沙410073
基金项目:国家863基金资助项目(2002AA001006)
摘    要:运载火箭最优上升轨道设计问题是一类终端时刻未定、终端约束苛刻的最优控制问题,经典算法求解这类问题时收敛性差、局部收敛等问题表现得比较突出。针对上述问题,将具有良好全局收敛性的遗传算法应用到运载火箭最优上升段设计问题求解中,为了提高遗传算法的收敛速度和克服早熟问题,结合遗传算法和单纯型算法的优点,设计了两种混合遗传算法。计算结果表明,所设计的混合遗传算法是求解复杂问题的有效全局优化方法,可以成功地解决一类终端时刻可变飞行器最优控制问题。

关 键 词:最优上升轨道设计  混合遗传算法  全局优化  串行和嵌套混合
文章编号:1001-2486(2004)02-0005-04
收稿时间:2003/9/30 0:00:00
修稿时间:2003年9月30日

Hybrid Genetic Algorithm and Its Application to the Optimal Ascent Trajectory Design of the Vehicle
LUO Yazhong,TANG Guojin and ZHOU Lini.Hybrid Genetic Algorithm and Its Application to the Optimal Ascent Trajectory Design of the Vehicle[J].Journal of National University of Defense Technology,2004,26(2):5-8.
Authors:LUO Yazhong  TANG Guojin and ZHOU Lini
Institution:College of Aerospace and Materials Engineering, National University of Defense Technology, Changsha 410073, China;College of Aerospace and Materials Engineering, National University of Defense Technology, Changsha 410073, China;College of Aerospace and Materials Engineering, National University of Defense Technology, Changsha 410073, China
Abstract:The optimal ascent trajectory design of the vehicle is an optimal control problem with strict terminal constraints and variable final time. The classical algorithms always encounter the problems of high sensitivity to initial guess and local convergence in solving this problem. Aiming at these problems, genetic algorithm (GA) which is of good global convergence is applied to designing the optimal ascent trajectory .In order to improve the convergence rate of GA and overcome its premature problems, two hybrid GA (HGA) combining the merits of GA with those of simplex method are proposed. The computational results testify that the two HGA are effective global optimization approach for solving complex problems, and they can successfully solve the optimal control problems with variable final time.
Keywords:optimal ascent trajectory design  hybrid genetic algorithm  global optimization  pipeline and nesting hybrid
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