Satellite constellation design optimization via multiple-objective evolutionary computation

Multiple-objective evolutionary computation provides the satellite constellation designer with an essential optimization tool due to the discontinuous, temporal, and/or nonlinear characteristics of the metrics that architectures are evaluated against. In this work, the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is utilized to generate sets of constellation designs (Pareto-fronts) that show the trade-off for two pairs of conflicting metrics. The first pair replicates a previously published sparse-coverage trade-off to establish a baseline for tool development, while the second characterizes the conflict between temporal (revisit time) and spatial (image quality) resolution. A thorough parameter analysis is performed on the NSGA-II for the constellation design problem so that the utility of the approach may be assessed and general guidelines for use established. The approximated Pareto-fronts generated for each trade-off are discussed and the trends exhibited by the non-dominated designs are revealed.