We consider an operational model of suicide bombing attacks –an increasingly prevalent form of terrorism– against specific targets, and the use of protective countermeasures based on the deployment of detectors over the area under threat.
These detectors have to be carefully located in order to minimize the expected number of casualties or the economic damage suffered, resulting in a hard optimization problem for which different metaheuristics have been proposed. Rather than assum-
ing random decisions by the attacker, the problem is approached by considering different models of the latter, whereby he takes informed decisions on which objective must be targeted and through which path it has to be reached based on knowledge on
the importance or value of the objectives or on the defensive strategy of the defender (a scenario that can be regarded as an adversarial game). We consider four different algorithms, namely a greedy heuristic, a hill climber, tabu search and an evolutionary
algorithm, and study their performance on a broad collection of problem instances trying to resemble different realistic settings such as a coastal area, a modern urban area, and the historic core of an old town. It is shown that the adversarial scenario is
harder for all techniques, and that the evolutionary algorithm seems to adapt better to the complexity of the resulting search landscape.
