Suicide bombing is one of the prevalent ways in which terrorist organizations substantiate their infamous actions. The defense from this kind of attacks is a highly cross-disciplinary endeavor that can be approached from many points of views. From a tactical point of view, it is often useful to analyze it from a political-rational perspective, that is, using a cost-benefit analysis analogous to the rational calculations of a military staff or a corporate board.
Some works in the literature have taken some steps in this direction. We have built on our previous research in which we focused on the problem of determining the distribution of detectors on a given area under threat, aiming to maximize the probability of detecting and neutralizing suicide bombers. This problem, seminally approached by greedy techniques in the literature, was tackled with metaheuristics in our work
As in the previous literature, the simplistic assumption that the terrorist attacker would pick a certain target and the path to reach it uniformly at random was used. We have taken a step beyond this premise, and considered the problem from two perspectives. In the first one, the terrorist attacker takes weighted decisions based on the relative importance of each target. In the second one, we take a game-theory perspective: two parties are involved, the attacker and the defender; the defender tries to place detectors so as to minimize the inflicted damage received by the attacker; conversely the attacker tries to select the target and the path to reach it so as to maximize this inflicted damage, knowing that the defender will try to negate these attempts. From the point of view of the defender that has to place the detectors, this amounts to minimize losses in a worst-case scenario.
We have approached the resulting problem in a recent paper entitled
considering hill climbing, tabu search and evolutionary algorithms. The evolutionary algorithms reveals itself as particularly well-adapted for this problem when the worst-case perspective (which provides a more challenging optimization scenario) is used. The full abstract of the paper follows:
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 assuming 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.