Advertisement

Journal of Heuristics

, Volume 24, Issue 3, pp 483–513 | Cite as

Metaheuristic approaches to the placement of suicide bomber detectors

  • Carlos CottaEmail author
  • José E. Gallardo
Article

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 (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.

Keywords

Counter-terrorism Suicide bombing Optimal detector placement Greedy heuristics Metaheuristics 

Notes

Acknowledgements

We would like to thank Mr. Antonio Hernández Bimbela for his help during the initial stage of this project, and to the anonymous reviewers for useful comments.

References

  1. Aarts, E.H.L., Lenstra, J.K.: Local Search in Combinatorial Optimization. Wiley, New York (1997)zbMATHGoogle Scholar
  2. AFP: Death toll from Baghdad blast rises to 292: minister. France 24. http://www.france24.com/en/20160707-death-toll-baghdad-blast-rises-292-minister (2016) . Accessed 15 July 2016
  3. Arechavaleta, G., Laumond, J.P., Hicheur, H., Berthoz, A.: An optimality principle governing human walking. IEEE Trans. Robot. 24(1), 5–14 (2008)CrossRefGoogle Scholar
  4. Carson, J.V.: Assessing the effectiveness of high-profile targeted killings in the “war on terror”. Criminol. Public Policy 16(1), 191–220 (2017)MathSciNetCrossRefGoogle Scholar
  5. Caygill, J.S., Davis, F., Higson, S.P.: Current trends in explosive detection techniques. Talanta 88, 14–29 (2012)CrossRefGoogle Scholar
  6. Chicago Project on Security and Terrorism (CPOST): suicide attack database (April 19, 2016 release). http://cpostdata.uchicago.edu/ (2016)
  7. Cotta, C., Fernández, A.J.: A hybrid GRASP - evolutionary algorithm approach to golomb ruler search. In: Yao, X., et al. (eds.) Parallel Problem Solving from Nature —PPSN VIII. Lecture Notes in Computer Science, vol. 3242, pp. 481–490. Springer (2004)Google Scholar
  8. De Jong K.A., Potter, M.A., Spears, W.M.: Using problem generators to explore the effects of epistasis. In: Bäck, T. (ed.) Proceedings of the 7th International Conference on Genetic Algorithms. Morgan Kaufmann, pp. 338–345 (1997)Google Scholar
  9. Edwards, D., McMenemy, L., Stapley, S., Patel, H., Clasper, J.: 40 years of terrorist bombings–a meta-analysis of the casualty and injury profile. Injury 47(3), 646–652 (2016)CrossRefGoogle Scholar
  10. Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Springer, Berlin (2003)CrossRefzbMATHGoogle Scholar
  11. Farahani, R.Z., Asgari, N., Heidari, N., Hosseininia, M., Goh, M.: Covering problems in facility location: a review. Comput. Ind. Eng. 62(1), 368–407 (2012)CrossRefGoogle Scholar
  12. Feo, T.A., Resende, M.G.C.: Greedy randomized adaptive search procedures. J. Glob. Optim. 6(2), 109–133 (1995)MathSciNetCrossRefzbMATHGoogle Scholar
  13. Festa, P., Resende, M.G.C.: An annotated bibliography of GRASP—Part I: algorithms. Int. Trans. Oper. Res. 16(1), 1–24 (2009a)MathSciNetCrossRefzbMATHGoogle Scholar
  14. Festa, P., Resende, M.G.C.: An annotated bibliography of GRASP—Part II: applications. Int. Trans. Oper. Res. 16(2), 131–172 (2009b)MathSciNetCrossRefzbMATHGoogle Scholar
  15. Friedman, M.: The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J. Am. Stat. Assoc. 32(200), 675–701 (1937)CrossRefzbMATHGoogle Scholar
  16. Friedman, M.: A comparison of alternative tests of significance for the problem of \(m\) rankings. Ann. Math. Stat. 11(1), 86–92 (1940)MathSciNetCrossRefzbMATHGoogle Scholar
  17. García, S., Herrera, F.: An extension on “statistical comparisons of classifiers over multiple data sets” for all pairwise comparisons. J. Mach. Learn. Res. 9, 2677–2694 (2008)zbMATHGoogle Scholar
  18. Gares, K.L., Hufziger, K.T., Bykov, S.V., Asher, S.A.: Review of explosive detection methodologies and the emergence of standoff deep UV resonance raman. J. Raman Spectrosc. 47(1), 124–141 (2016)CrossRefGoogle Scholar
  19. Hoffman, B.: The logic of suicide terrorism. The Atlantic. http://www.theatlantic.com/magazine/archive/2003/06/the-logic-of-suicide-terrorism/302739/ (2003). Accessed 2 July 2016
  20. Hoos, H., Stützle, T.: Stochastic Local Search. Foundations and Applications. Morgan Kaufmann, Burlington (2005)zbMATHGoogle Scholar
  21. Kaplan, E.H., Kress, M.: Operational effectiveness of suicide-bomber-detector schemes: a best-case analysis. Proc. Natl. Acad. Sci. USA 102(29), 10,399–10,404 (2005)CrossRefGoogle Scholar
  22. Kim, M., Batta, R., He, Q.: Optimal routing of infiltration operations. J. Transp. Secur. 9(1), 87–104 (2016)CrossRefGoogle Scholar
  23. Kroenig, M., Pavel, B.: How to deter terrorism. Wash. Q. 35(2), 21–36 (2012)CrossRefGoogle Scholar
  24. Kurukız, H.Ş.: Istanbul airport terror attack death toll rises to 44. Anadolou Agency. http://aa.com.tr/en/todays-headlines/istanbul-airport-terror-attack-death-toll-rises-to-44/599383 (2016) . Accessed 2 July 2016
  25. Larrañaga, P., Lozano, J.A.: Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation, vol. 2. Springer, New York (2002)zbMATHGoogle Scholar
  26. Mühlenbein, H., Paaß, G.: From recombination of genes to the estimation of distributions I. Binary parameters. In: Voigt, H., Ebeling, W., Rechenberg, I., Schwefel, H. (eds.) Parallel Problem Solving from Nature—PPSN IV. Lecture Notes in Computer Science, vol. 1141, pp. 178–187. Springer, Berlin Heidelberg (1996)Google Scholar
  27. Neri, F., Cotta, C., Moscato, P. (eds.): Handbook of Memetic Algorithms, Studies in Computational Intelligence, vol. 379. Springer, Berlin Heidelberg (2012)Google Scholar
  28. Nie, X., Batta, R., Drury, C.G., Lin, L.: Optimal placement of suicide bomber detectors. Mil. Oper. Res. 12(2), 65–78 (2007)CrossRefGoogle Scholar
  29. NRC: Existing and potential standoff explosives detection techniques. National Research Council of the National Academies, Natl. Acad. Press, Washington (2004)Google Scholar
  30. Rand Corporation: RAND database of worldwide terrorism incidents. http://smapp.rand.org/rwtid/search_form.php (2009)
  31. Shaffer, J.P.: Modified sequentially rejective multiple test procedures. J. Am. Stat. Assoc. 81(395), 826–831 (1986)CrossRefzbMATHGoogle Scholar
  32. Singh, S.: Sensors—an effective approach for the detection of explosives. J. Hazard. Mater. 144(1–2), 15–28 (2007)CrossRefGoogle Scholar
  33. Yan, X., Nie, X.: Optimal placement of multiple types of detectors under a small vessel attack threat to port security. Transp. Res. Part E Logist. Transp. Rev. 93, 71–94 (2016)CrossRefGoogle Scholar
  34. Yinon, J.: Counterterrorist Detection Techniques of Explosives. Elsevier, Amsterdam (2007)Google Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Department Lenguajes y Ciencias de la ComputaciónUniversidad de MálagaMálagaSpain

Personalised recommendations