Metaheuristic Approaches to the Placement of Suicide Bomber Detectors.

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 (such as a hill climber, different Greedy randomized adaptive search procedures, an evolutionary algorithm and several estimation of distribution algorithms) 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.Spanish Ministry of Economy and Competitiveness and European Regional Development Fund (FEDER) under project EphemeCH (TIN2014-56494-C4-1-P)

New Perspectives on the Optimal Placement of Detectors for Suicide Bombers using Metaheuristics.

Política de acceso abierto tomada de: https://www.springernature.com/gp/open-research/policies/journal-policies y https://beta.sherpa.ac.uk/id/publication/16694We 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.Spanish Ministerio de Economı́a and European FEDER under Projects EphemeCH (TIN2014-56494-C4-1-P) and DeepBIO (TIN2017-85727-C4-1-P