Efficient and Accurate Evolutionary Multi-Objective Optimization Paradigms for Satellite Constellation Design

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.