An adaptive genetic algorithm for solving ground-space TT&C resources integrated scheduling problem of Beidou constellation

Space-based TT&C technology is an effective way to solve the problem of resources dissatisfaction of ground-based TT&C system. When solving the Beidou MEO constellation optimization scheduling problem, traditional genetic algorithm (GA) has the disadvantages of premature and low speed convergence. This paper designs a self-adjust based GA which adds an evolution probability principle which depends on population diversity, population fitness and population generation number. Meanwhile, when to select new population, it adopts refine management and elite preservation strategy of divisional sampling so as to enhance the search performance of GA The experimental result demonstrates the validity of the new algorithm. Compared with the traditional GA, the new algorithm increases the schedule completion rate and weighted task completion rate by 11% and 11.1 % respectively.