The configuration of radar networks is a complex problem that is often performed manually by experts with the help of a simulator. Different numbers and types of radars as well as different locations that the radars shall cover give rise to different instances of the radar configuration problem. The exact modeling of these instances is complex, as the quality of the configurations depends on a large number of parameters, on internal radar processing, and on the terrains on which the radars need to be placed. Classic optimization algorithms can therefore not be applied to this problem, and we rely on "trial-and-error" black-box approaches. In this paper, we study the performances of 13~black-box optimization algorithms on 153~radar network configuration problem instances. The algorithms perform considerably better than human experts. Their ranking, however, depends on the budget of configurations that can be evaluated and on the elevation profile of the location. We therefore also investigate automated algorithm selection approaches. Our results demonstrate that a pipeline that extracts instance features from the elevation of the terrain performs on par with the classical, far more expensive approach that extracts features from the objective function.
翻译:雷达网络的配置是一个复杂的问题,通常由专家在模拟器的帮助下手工操作。 雷达的不同数量和类型以及雷达应覆盖的不同位置都会产生不同的雷达配置问题。 精确地模拟这些情况是复杂的, 因为配置的质量取决于大量的参数, 取决于内部雷达处理, 取决于雷达需要放置的地形。 因此, 经典优化算法不能适用于这一问题, 我们依靠“ 审判和机” 黑盒方法。 在本文中, 我们研究了 13 ~ 黑盒优化算法的性能, 以153 ~ 雷达网络配置问题为例。 算法的性能比人类专家要好得多。 但是, 它们的排名取决于能够评估的配置预算以及位置的高度配置。 因此, 我们还调查自动算法选择方法。 我们的结果表明, 从地形上升中提取实例特征的管道与从客观功能中提取特征的古典化方法相同, 费用要高得多。