In this paper, we consider a multi-purpose two-level location problem introduced by Karatas (2020) to improve the coverage performance of heterogeneous sensor networks. The problem basically seeks to determine the best location scheme of sensors of different types and characteristics in a belt-shaped boundary area with the purpose of providing a sufficient level of field, point and barrier coverage against different types of intruders. To solve the problem, first, the Mixed Integer Linear Programming (MILP) model developed by Karatas (2020) is used together with a commercial solver and a Non-Dominated Sorting Genetic Algorithm-II (NSGA-II), adapted to solve especially large-sized instances of the problem, is applied. Next, we compare the NSGA-II heuristic with the MILP solved via a commercial exact solver on a number of test instances. The experiment results suggest that the heuristic algorithm can produce a large number of diverse and high-quality solutions in very short computation times in comparison to the exact solver.

在本文中，我们考虑了Karatas（2020）提出的多用途两级定位问题，以提高异构传感器网络的覆盖性能。该问题主要是寻求在带状边界区域中确定不同类型和特性的传感器的最佳定位方案，目的是针对不同类型的入侵者提供足够水平的场，点和障碍物。为了解决这个问题，首先，将由Karatas（2020）开发的混合整数线性规划（MILP）模型与商业求解器和非支配排序遗传算法II（NSGA-II）一起使用，以解决特别大的问题。问题的大小实例被应用。接下来，我们将NSGA-II启发式算法与通过商业精确求解器在许多测试实例上求解的MILP进行比较。