Multiobjective optimization of geosynchronous earth orbit space situational awareness systems via parallel executable architectures

Abstract

This research implements a genetic algorithm (GA) to optimize geosynchronous Earth orbit (GEO) space situational awareness (SSA) systems via parallel evaluation of executable architectures on a high-performance computer (HPC). This effort has two main goals. The first is to develop and validate a methodology for optimization of large systems-of-systems in a robust manner that limits the assumptions typically necessary while performing large architecture trade studies. The second goal is to determine the set of near-optimal solutions for a GEO SSA system across multiple objectives. The GA is implemented in Python, and architectures are modeled and simulated with AGI's Systems Tool Kit (STK)™ on an Air Force Research Laboratory HPC. Results show how the GA finds increasingly "good" solutions, where the multiple objectives can be weighted and filtered. After 319,968 architectures were modeled, simulated, and evaluated on the HPC (27 years of CPU time, 3 days clock time), the near-optimal solution consisted of 10 globally distributed 1-m telescopes, 4 satellites in 1000 km equatorial low Earth orbit with 30-cm sensor apertures, and 3 satellites in GEO with 45-cm sensor apertures.