基于多目标元启发式算法的低轨卫星波束调度优化方法OA

In scenarios where communication traffic demand is highly unevenly distributed,the application of efficient dynamic beam scheduling methods can significantly enhance the system capacity of low Earth orbit(LEO)communication satellites.Based on the existing onboard phased array multi-beam antenna architecture of LEO satellites,an optimization problem model for LEO satellite beam scheduling problem is provided by formulating the optimization variables and constrained conditions.The computational complexity analysis of this optimization problem confirms its NP-hard essence.To address the NP-hardness of the problem and provide a more diverse set of options for actual satellite beam scheduling scenarios,Non-dominated Sorting Genetic Algorithms-Ⅱ(NSGA-Ⅱ)and Strength Pareto Evolutionary Algorithm 2(SPEA2),which are based on principles of genetic algorithm,are utilized to optimize beam scheduling results aiming to covers more communication terminals while reducing inter-beam interference.In the numerical simulation results,the approximate Pareto solutions calculated by NSGA-Ⅱ and SPEA2 are 19 and 25 respectively.In terms of the performance comparison of the corner solution,there is only an 8％difference between NSGA-Ⅱ and SPEA2 in the interference avoidance specification,but the coverage traffic specification of NSGA-Ⅱ exceeds that of SPEA2 by 30％.