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星际转移轨道设计的差分进化优化工具(CS AI)

星际转移轨道设计中极其敏感和高度非线性的搜索空间给全局优化带来了巨大挑战。作为代表,目前已知的由欧洲航天局(ESA)设计的全球轨道优化问题(GTOP)的最佳解是很难找到的。为了解决这一难题,本文提出了一种基于差分进化的优化工具——协同差分进化。代码采用两阶段进化过程,在早期集中学习全局结构,并倾向于自适应学习不同局部空间的结构。此外,考虑到不同问题全局最优解的空间分布和不同变量的梯度信息,采用了多重边界检测技术。此外,协方差矩阵适应进化策略(CMA-ES)被用作局部优化器。以往的研究表明,一个特定的群体智能优化算法通常只能解决一两个GTOP问题。但实验测试结果表明,CODE可以直接找到目前已知的Cassini1和Sagas的最佳解,与CMA-ES的合作可以求解Cassini2、GTOC1、Messenger (reduced)和Rosetta。对于最复杂的Messenger (full)问题,即使CODE找不到当前已知的最优解,但找到的目标函数等于3.38 km/s的最优解仍然是其他群体智能算法无法轻易达到的水平。

原文题目:A differential evolution-based optimization tool for interplanetary transfer trajectory design

原文:The extremely sensitive and highly nonlinear search space of interplanetary transfer trajectory design bring about big challenges on global optimization. As a representative, the current known best solution of the global trajectory optimization problem (GTOP) designed by the European space agency (ESA) is very hard to be found. To deal with this difficulty, a powerful differential evolution-based optimization tool named COoperative Differential Evolution (CODE) is proposed in this paper. CODE employs a two-stage evolutionary process, which concentrates on learning global structure in the earlier process, and tends to self-adaptively learn the structures of different local spaces. Besides, considering the spatial distribution of global optimum on different problems and the gradient information on different variables, a multiple boundary check technique has been employed. Also, Covariance Matrix Adaptation Evolutionary Strategies (CMA-ES) is used as a local optimizer. The previous studies have shown that a specific swarm intelligent optimization algorithm usually can solve only one or two GTOP problems. However, the experimental test results show that CODE can find the current known best solutions of Cassini1 and Sagas directly, and the cooperation with CMA-ES can solve Cassini2, GTOC1, Messenger (reduced) and Rosetta. For the most complicated Messenger (full) problem, even though CODE cannot find the current known best solution, the found best solution with objective function equaling to 3.38 km/s is still a level that other swarm intelligent algorithms cannot easily reach.

原文作者:Mingcheng Zuo,Guangming Daia, Lei Penga, Zhe Tang

原文地址:https://arxiv.org/abs/2011.06780

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