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首页> 外文期刊>Mathematical Problems in Engineering: Theory, Methods and Applications >Spacecraft Multiple-Impulse Trajectory Optimization Using Differential Evolution Algorithm with Combined Mutation Strategies and Boundary-Handling Schemes
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Spacecraft Multiple-Impulse Trajectory Optimization Using Differential Evolution Algorithm with Combined Mutation Strategies and Boundary-Handling Schemes

机译:结合变异策略和边界处理方案的差分进化算法优化航天器多脉冲轨迹

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摘要

Since most spacecraft multiple-impulse trajectory optimization problems are complex multimodal problems with boundary constraint, finding the global optimal solution based on the traditional differential evolution (DE) algorithms becomes so difficult due to the deception of many local optima and the probable existence of a bias towards suboptimal solution. In order to overcome this issue and enhance the global searching ability, an improved DE algorithm with combined mutation strategies and boundary-handling schemes is proposed. In the first stage, multiple mutation strategies are utilized, and each strategy creates a mutant vector. In the second stage, multiple boundary-handling schemes are used to simultaneously address the same infeasible trial vector. Two typical spacecraft multiple-impulse trajectory optimization problems are studied and optimized using the proposed DE method. The experimental results demonstrate that the proposed DE method efficiently overcomes the problem created by the convergence to a local optimum and obtains the global optimum with a higher reliability and convergence rate compared with some other popular evolutionary methods.
机译:由于大多数航天器多脉冲轨迹优化问题是具有边界约束的复杂多峰问题,由于许多局部最优的欺骗和可能存在的偏差,因此基于传统的差分进化(DE)算法寻找全局最优解变得非常困难。朝向次优解决方案。为了克服这一问题并增强全局搜索能力,提出了一种结合变异策略和边界处理方案的改进DE算法。在第一阶段,利用多种突变策略,每种策略创建一个突变载体。在第二阶段,使用多个边界处理方案来同时处理相同的不可行试验向量。使用提出的DE方法研究和优化了两个典型的航天器多脉冲轨迹优化问题。实验结果表明,与其他流行的进化方法相比,提出的DE方法有效地克服了收敛到局部最优所产生的问题,并以更高的可靠性和收敛速度获得了全局最优。

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