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首页> 外文期刊>Journal of Spacecraft and Rockets >Efficient and Accurate Evolutionary Multi-Objective Optimization Paradigms for Satellite Constellation Design
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Efficient and Accurate Evolutionary Multi-Objective Optimization Paradigms for Satellite Constellation Design

机译：卫星星座设计的高效，精确的进化多目标优化范例

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摘要

Multi-objective evolutionary algorithms have been shown to be effective optimization tools to search the complex tradeoff spaces of satellite constellation design. Often, the metrics that make up the design tradeoff require lengthy function evaluation time, resulting in a decreased utility of serial multi-objective evolutionary algorithms. In this research, the authors implement two parallel processing multi-objective evolutionary algorithm paradigms, the master-slave and island models, on a heterogeneous system of processors and operating systems. The efficiency and effectiveness of each approach is studied in the context of a regional coverage design problem. The island scheme outperforms the master-slave model with respect to efficiency. A study of the search dynamics for each paradigm demonstrates that both reliably meet the goals of multi-objective optimization (progressing toward the Pareto-optimal front while maintaining a diverse set of solutions). A key conclusion of this research is that both paradigms provide excellent approximations of the true Pareto frontier using a single seed, and when combined across multiple trial runs, they find nearly the entire set of Pareto-optimal solutions.
机译：多目标进化算法已被证明是搜索卫星星座设计复杂权衡空间的有效优化工具。通常，构成设计折衷的指标需要较长的功能评估时间，导致串行多目标进化算法的效用降低。在这项研究中，作者在处理器和操作系统的异构系统上实现了两个并行处理的多目标进化算法范例，即主从模型和孤岛模型。在区域覆盖设计问题的背景下研究每种方法的效率和有效性。就效率而言，孤岛方案优于主从模型。对每种范例的搜索动力学的研究表明，两者都可以可靠地满足多目标优化的目标（在保持各种解决方案的同时，朝着帕累托最优阵线发展）。这项研究的主要结论是，这两种范例都使用单个种子提供了真正的帕累托边界的极佳近似值，并且在进行多次试验运行时，它们几乎找到了整个帕累托最优解集。

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