  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.
