An evolutionary framework for automatic security guards deployment in large public spaces (original) (raw)

Abstract

The deployment of security guards in large public spaces is a promising research topic with a wide range of applications. Existing methods are mainly based on manual design approaches, which are neither effective nor flexible enough for large-scale scenarios. To address this issue, this paper proposes an evolutionary framework to automatically generate the optimal deployment strategy of security guards in large public spaces. The proposed method includes a new metric for automatically evaluating deployment strategies, as well as an evolutionary solver based on differential evolution to optimize the deployment strategy automatically. To evaluate its effectiveness, the proposed evolutionary framework is tested on two synthetic scenarios with different characteristics and one real-world scenario. The results demonstrate that the proposed framework outperforms several commonly used strategies in terms of the response time of security guards.

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References

  1. Tan S, Luo Y (2018) Flexible security guard scheduling to satisfy defensive power by tabu-search algorithm. In: Proceedings of the 2nd international conference on vision, image and signal processing, pp 1–5. Association for Computing Machinery. https://doi.org/10.1145/3271553.3271586
  2. Suresh MSS, Narayanan A, Menon V (2020) Maximizing camera coverage in multicamera surveillance networks. IEEE Sensors J 20(17):10170–10178
    Article Google Scholar
  3. Han Z, Li S, Cui C, Song H, Kong Y, Qin F (2019) Camera planning for area surveillance: a new method for coverage inference and optimization using location-based service data. Comput Environ Urban Syst 78:101396–101411
    Article Google Scholar
  4. Jayakody A, Lokuliyana S, Dasanayaka K, Iddamalgoda A, Ganepola I, Dissanayake A (2021) i-police - an intelligent policing system through public area surveillance. In: 2021 IEEE 12th annual information technology, electronics and mobile communication conference (IEMCON), pp 0148–0154. https://doi.org/10.1109/IEMCON53756.2021.9623145
  5. Wilson OW, McLaren RC (1963) Police administration, vol 139. McGraw-Hill, New York
    Google Scholar
  6. Adams TF (1990) Police field operations. Prentice-Hall
  7. Jiang C, Kusakunniran W, Pomprasatpol N, Limsuwankeson C, Li Y (2017) Smart security guard scheduling system based on the reinforcement learning. In: 2017 21st International computer science and engineering conference (ICSEC), pp 1–5
  8. Chen H, Cheng T, Wise S (2017) Developing an online cooperative police patrol routing strategy. Comput Environ Urban Syst 62:19–29
    Article Google Scholar
  9. Chawathe SS (2007) Organizing hot-spot police patrol routes. In: 2007 IEEE Intelligence and security informatics, pp 79–86. https://doi.org/10.1109/ISI.2007.379538
  10. Keskin BB, Li SR, Steil D, Spiller S (2012) Analysis of an integrated maximum covering and patrol routing problem. Transp Res Part E: Logistics Transp Rev 48(1):215–232. https://doi.org/10.1016/j.tre.2011.07.005. Select Papers from the 19th International Symposium on Transportation and Traffic Theory
    Article Google Scholar
  11. Chainey SP, Matias JAS, Nunes Junior FCF, Coelho da Silva TL, de Macêdo JAF, Magalhães RP, de Queiroz Neto JF, Silva WCP (2021) Improving the creation of hot spot policing patrol routes: comparing cognitive heuristic performance to an automated spatial computation approach. ISPRS Int J Geo-Inform 10(8):560
    Article Google Scholar
  12. Clawson C, Chang SK (1977) The relationship of response delays and arrest rates. J Police Sci Adm 5(1):53–68
    Google Scholar
  13. Cihan A, Zhang Y, Hoover L (2012) Police response time to in-progress burglary: a multilevel analysis. Police Quarterly 15(3):308–327
    Article Google Scholar
  14. Eck JE, Rosenbaum D (1994) The new police order: effectiveness, equity, and efficiency in community policing. The challenge of community policing: Testing the promises, 3–23
  15. Qu Y, Ma Z, Clausen A, Jørgensen BN (2021) A comprehensive review on evolutionary algorithm solving multi-objective problems. In: 2021 22nd IEEE International conference on industrial technology (ICIT), vol 1, pp 825–831
  16. Fogel DB (1994) An introduction to simulated evolutionary optimization. IEEE Trans Neural Netw 5(1):3–14
    Article Google Scholar
  17. Zhong J, Cai W (2015) Differential evolution with sensitivity analysis and the powell’s method for crowd model calibration. J Comput Sci 9:26–32. Computational Science at the Gates of Nature
    Article Google Scholar
  18. Zhong J, Hu N, Cai W, Lees M, Luo L (2015) Density-based evolutionary framework for crowd model calibration. J Comput Sci 6:11–22
    Article Google Scholar
  19. Lu K, Zhou W, Zeng G, Zheng Y (2019) Constrained population extremal optimization-based robust load frequency control of multi-area interconnected power system. Int J Electr Power Energy Syst 105:249–271. https://doi.org/10.1016/j.ijepes.2018.08.043https://doi.org/10.1016/j.ijepes.2018.08.043
    Article Google Scholar
  20. Jiang Y, Li H, Feng B, Wu Z, Zhao S, Wang Z (2022) Street patrol routing optimization in smart city management based on genetic algorithm: a case in Zhengzhou, China. ISPRS Int J Geo-Inform 11 (3):171
    Article Google Scholar
  21. Storn R, Price K (1997) Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359
    Article MathSciNet MATH Google Scholar
  22. Bilal, Pant M, Zaheer H, Garcia-Hernandez L, Abraham A (2020) Differential evolution: a review of more than two decades of research. Eng Appl Artif Intel 90:103479
    Article Google Scholar
  23. Kitamura T, Fukunaga A (2020) Revisiting success-histories for adaptive differential evolution. In: 2020 IEEE Congress on evolutionary computation (CEC), pp 1–8
  24. Ahmad MF, Isa NAM, Lim WH, Ang KM (2021) Differential evolution: a recent review based on state-of-the-art works. Alexandria Engineering Journal. https://doi.org/10.1016/j.aej.2021.09.013
  25. Helbing D, Farkas I, Vicsek T (2000) Simulating dynamical features of escape panic. Nature 407(6803):487–490
    Article Google Scholar
  26. Civicioglu P, Besdok E (2013) A conceptual comparison of the cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms. Artif Intell Rev 39(4):315–346
    Article Google Scholar
  27. Jones KO, Boizanté G (2011) Comparison of firefly algorithm optimisation, particle swarm optimisation and differential evolution. In: Proceedings of the 12th international conference on computer systems and technologies. CompSysTech ’11, pp 191–197. Association for Computing Machinery
  28. Iwan M, Akmeliawati R, Faisal T, Al-Assadi HMAA (2012) Performance comparison of differential evolution and particle swarm optimization in constrained optimization. Procedia Eng 41:1323–1328
    Article Google Scholar
  29. Koenig S, Likhachev M, Furcy D (2004) Lifelong planning a*. Artif Intell 155(1):93–146
    Article MathSciNet MATH Google Scholar
  30. Zhong J, Cai W, Lees M, Luo L (2017) Automatic model construction for the behavior of human crowds. Appl Soft Comput 56:368–378
    Article Google Scholar
  31. Zhu R, Aqlan F, Yang H (2022) Optimal resource allocation for coverage control of city crimes. In: Yang H, Qiu R, Chen W (eds) AI and analytics for public health, pp 149–161. Springer

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (Grant No. 62076098), the Guangdong Natural Science Foundataion Research Team (Grant No. 2018B030312003), the GuangDong Basic and Applied Basic Research Foundation (Grant No. 2021A1515110072), and the research start-up funds of Guangdong Polytechnic Normal University (Grant No. 2021SDKYA130).

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Authors and Affiliations

  1. School of Computer Science and Engineering, South China University of Technology, No.382 Waihuan Dong Road, Guangzhou, 510006, Guangdong, China
    Zhitong Ma & Jinghui Zhong
  2. School of Computer Science, Guangdong Polytechnic Normal University, No.293 West Zhongshan Road, Guangzhou, 510665, Guangdong, China
    Wei-Li Liu
  3. School of Information Management, Sun Yat-Sen University, No.135 Xingang Road West Road, Guangzhou, 510275, Guangdong, China
    Wei-Jie Yu

Authors

  1. Zhitong Ma
  2. Jinghui Zhong
  3. Wei-Li Liu
  4. Wei-Jie Yu

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Correspondence toJinghui Zhong or Wei-Li Liu.

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Ma, Z., Zhong, J., Liu, WL. et al. An evolutionary framework for automatic security guards deployment in large public spaces.Appl Intell 53, 11586–11598 (2023). https://doi.org/10.1007/s10489-022-03975-6

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