Efficient Constellation Design Based on Improved Non-dominated Sorting Genetic Algorithm-II

Constellation design is a typical multiple peaks, multiple valleys and non-linear multi-objective optimization problem. How to design satellite constellation is one of the key sectors of research in the aerospace field. In this paper, in order to improve the global convergence and diversity performance of traditional constellation optimization algorithm, multi-parent arithmetic crossover and SBX crossover operator of NSGA-II are used to improve searching capability of this algorithm. Meanwhile, Gaussian mutation and Cauchy mutation, with diversity of population, make the algorithm get better behaviors in convergence and diversity of finding solutions. Based on the methods, an improvement NSGA-II is presented to design constellation in the paper. The algorithm uses fixed length chromosome representation. Real coding is adopted for that the problem has both integer continuous variables. Combining the coverage assessment criterions, an orbit parameters optimization framework based on nondominated sorting genetic algorithm (NSGA-II) was proposed. This method is applied to a detailed example, and result shows that a group of Pareto solutions with good spread can be achieved, which gives strong support to constellation scheme determination.