This research explores a parallel implementation of a genetic algorithm (GA) to optimize geosynchronous orbit (GEO) Space Situational Awareness (SSA) architectures. It does so by parallel evaluation of executable (simulated) architectures on a high-performance computer (HPC). This effort had two primary objectives. The first was to develop and validate a robust methodology for design optimization of space constellations that avoids the typical limitations of point solutions, performance approximations, or limited design parameters. The second objective was to determine the near-optimal GEO SSA solution across multiple realistic objectives. The GA was implemented in Python, and architectures were modeled and simulated with AGI’s Systems Tool Kit (STK)™ on a 4600-node high-performance computing cluster. Results show that the GA finds increasingly “good” solutions in a 28-dimension design space and across 3 objectives, and these solutions were examined for sensitivity to preferences. After nearly 320,000 architectures were modeled, simulated, and evaluated on the HPC (27 years of CPU time, 3 days clock time), the near-optimal “maximum performance” solution consisted of 19 globally distributed telescopes, 4 satellites in equatorial low Earth orbit, and 4 satellites in near-GEO. The solutions obtained by this research outperform the extant literature and provide insight into large-scale, multidomain, multi-orbit disaggregated constellation designs and specifically GEO SSA solutions.
Multi-Objective Parallel Optimization of Geosynchronous Space Situational Awareness Architectures
Journal of Spacecraft and Rockets ; 55 , 6 ; 1453-1465
01.11.2018
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Optimization of Geosynchronous Space Situational Awareness Architectures using Parallel Computation
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