A Novel Migrant PSO Algorithm for Vehicle Reentry Trajectory Optimization
A Novel Migrant PSO Algorithm for Vehicle Reentry Trajectory Optimization
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一种用于飞行器再入轨迹优化的新型移民 PSO 算法
DOI：
10.1166/jctn.2012.2022
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发表时间：
2012-02
影响因子：
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通讯作者：
Zheng, Zong-Zhun
中科院分区：
文献类型：
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作者：
Xie, Fu-Qiang;Wang, Yong-Ji;Hu, Cheng-Yu;Zheng, Zong-Zhun
提出了一种新的迁移粒子群优化算法（MigrantParticleSwarmOptimization，简称MigrantPSO），以提高多约束轨迹优化的性能。为了模拟一群候鸟的行为，迁移粒子群算法在连续空间和离散空间内有效地结合了随机搜索和自适应线性搜索机制。然后将该算法应用于X-33飞行器自由终端时间模型的最小控制能量再入轨迹优化，并对参数化方法等关键问题进行了详细讨论。通过对迁移粒子群算法和序列二次规划（SQP）算法的仿真结果比较，验证了该方法的有效性和高效性。
In this study a novel Migrant Particle Swarm Optimization (Migrant PSO) algorithm is presented to upgrade the performance in multi-constraint trajectory optimization. To imitate the behaviour of a flock of migrant birds, the Migrant PSO algorithm integrates stochastic search and adaptive linear search mechanism effectively within both continuous space and discrete space. Then the developed algorithm is applied to the minimum control energy reentry trajectory optimization for X-33 vehicle model with free terminal time, some key issues such as parameterized method are discussed in detail. The effectiveness and efficiency of the proposed method are demonstrated by the comparison between the simulation result of the Migrant PSO algorithm and that of the Sequential quadratic programming (SQP) method.