Suicide bombing is an infamous form of terrorism that is becoming increasingly prevalent in the current era of global terror warfare. We consider the case of targeted attacks of this kind, and the use of detectors distributed over the area under threat as a protective countermeasure. Such detectors are non-fully reliable, and must be strategically placed in order to maximize the chances of detecting the attack, hence minimizing the expected number of casualties. To this end, different metaheuristic approaches based on local search and on population-based search are considered and benchmarked against a powerful greedy heuristic from the literature. We conduct an extensive empirical evaluation on synthetic instances featuring very diverse properties. Most metaheuristics outperform the greedy algorithm, and a hill-climber is shown to be superior to remaining approaches. This hill-climber is subsequently subject to a sensitivity analysis to determine which problem features make it stand above the greedy approach, and is finally deployed on a number of problem instances built after realistic scenarios, corroborating the good performance of the heuristic.

翻译：自杀式炸弹袭击是恐怖主义的一种恶劣形式，在全球恐怖主义战争日益猖獗的当下愈发普遍。本文针对此类有预谋的袭击事件，探讨通过在受威胁区域分布式部署探测器作为防护性对策。此类探测器并非完全可靠，必须进行战略性布局，以最大化侦测到袭击的概率，从而最小化预期伤亡人数。为此，我们考虑了基于局部搜索和基于群体搜索的不同元启发式方法，并以文献中一种强大的贪心启发式算法作为基准进行比较。我们在具有高度多样化属性的合成实例上进行了广泛的实证评估。大多数元启发式方法的表现优于贪心算法，其中爬山算法被证明优于其他方法。随后对该爬山算法进行了敏感性分析，以确定哪些问题特征使其表现优于贪心方法，最后将其部署在基于现实场景构建的若干问题实例上，结果证实了该启发式算法的良好性能。