Constellation Design with Parallel Multi-Objective Evolutionary Computation
Matthew P. Ferringer, Ronald S. Clifton, Timothy G. Thompson · AIAA/AAS Astrodynamics Specialist Conference and Exhibit · 2006
‡Multi-objective evolutionary algorithms (MOEA) have been shown to be effective optimization tools to search the complex trade-off space s of satellite constellation design. Often the metrics that make up the design trade-off req uire lengthy function evaluation time, resulting in a decreased utility of serial MOEA. In this research the authors implement two parallel processing MOEA paradigms, the master-slave and island models, on a heterogeneous system of processors and operating syst ems. The efficiency and effectiveness of each approach is studied in the conte xt 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 towar ds the Pareto-optimal front while maintaining a diverse set of solutions). A key conclu sion 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 find nearly the entire set of Pareto-optimal solutions.