The ground station scheduling problem is a complex scheduling problem involving multiple objectives. Evolutionary techniques for multi-objective optimization are becoming popular among different fields, due to their effectiveness in obtaining a set of trade-off solutions. In contrast to some conventional methods, that aggregate the objectives into one weighted-sum objective function, multi-objective evolutionary algorithms manage to find a set of solutions in the Pareto-optimal front. Selecting one algorithm, however, for a specific problem adds additional challenge. In this paper the ground station scheduling problem was solved through six different evolutionary multi-objective algorithms, the NSGA-II, NSGA-III, SPEA2, GDE3, IBEA, and MOEA/D. The goal is to test their efficacy and performance to a number of benchmark static instances of the ground scheduling problem. Benchmark instances are of different sizes, allowing further testing of the behavior of the algorithms to different dimensionality of the problem. The solutions are compared to the recent solutions of a weighted-sum approach solved by the GA. The results show that all multi-objective algorithms manage to find as good solution as the weighted-sum, while giving more additional alternatives. The decomposition-based MOEA/D outperforms the rest of the algorithms for the specific problem in almost all aspects.

地面站调度问题是涉及多个目标的复杂调度问题。由于多目标优化的进化技术在获得一组权衡解决方案方面的有效性，因此它们在不同领域之间变得越来越流行。与将目标聚合为一个加权和目标函数的一些常规方法相比，多目标进化算法设法在帕累托最优前沿中找到一组解。然而，针对特定问题选择一种算法会增加其他挑战。本文通过六个不同的进化多目标算法NSGA-II，NSGA-III，SPEA2，GDE3，IBEA和MOEA / D解决了地面站调度问题。目的是针对地面调度问题的多个基准静态实例测试它们的功效和性能。基准实例的大小不同，从而可以针对问题的不同维度对算法的行为进行进一步测试。将这些解决方案与最近由GA解决的加权和方法的解决方案进行了比较。结果表明，所有多目标算法都设法找到与加权和一样好的解决方案，同时给出了更多其他选择。对于几乎所有方面的特定问题，基于分解的MOEA / D均优于其他算法。将这些解决方案与最近由GA解决的加权和方法的解决方案进行了比较。结果表明，所有多目标算法都设法找到与加权和一样好的解决方案，同时给出了更多其他选择。对于几乎所有方面的特定问题，基于分解的MOEA / D均优于其他算法。将这些解决方案与最近由GA解决的加权和方法的解决方案进行了比较。结果表明，所有多目标算法都设法找到与加权和一样好的解决方案，同时给出了更多其他选择。对于几乎所有方面的特定问题，基于分解的MOEA / D均优于其他算法。