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...

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Bibliographic Details
Published in:Journal of heuristics Vol. 24; no. 3; pp. 483 - 513
Main Authors: Cotta, Carlos, Gallardo, José E.
Format: Journal Article
Language:English
Published: New York Springer US 01.06.2018
Springer Nature B.V
Subjects:
Adaptive algorithms
Adaptive search techniques
Artificial Intelligence
Calculus of Variations and Optimal Control; Optimization
Casualties
Detectors
Empirical analysis
Evolutionary algorithms
Greedy algorithms
Heuristic methods
Management Science
Mathematics
Mathematics and Statistics
Operations Research
Operations Research/Decision Theory
Searching
Sensitivity analysis
Sensors
Suicide bombings
Terrorism
Warfare
Suicide bombing
Counter-terrorism
Greedy heuristics
Metaheuristics
Optimal detector placement
ISSN:1381-1231, 1572-9397
Online Access:Get full text
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Summary: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.
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ISSN:1381-1231
1572-9397
DOI:10.1007/s10732-017-9335-z