Metaheuristic approaches to the placement of suicide bomber detectors

TL;DR

This paper explores the use of metaheuristic algorithms for strategically placing suicide bomber detectors to maximize detection chances and minimize casualties, outperforming traditional greedy methods in diverse scenarios.

Contribution

It introduces and benchmarks various metaheuristic approaches for detector placement, demonstrating their superiority over existing greedy heuristics in complex, synthetic, and realistic scenarios.

Findings

Metaheuristics outperform greedy algorithms in detector placement.

Hill-climber approach is the most effective among tested methods.

Metaheuristics perform well on realistic scenario instances.

Abstract

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.