Skip to main content
SpringerLink
Log in

Pastoralist Optimization Algorithm (POA): A Culture-Inspired Metaheuristic for Uncapacitated Facility Location Problem (UFLP)

  • Conference paper
  • First Online:
Hybrid Intelligent Systems (HIS 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1375))

Included in the following conference series:

Abstract

In this paper, the performance of the recently developed Pastoralist Optimization Algorithm (POA) on classical uncapacitated Facility Location problem (UFLP) was investigated. POA is a culture-inspired metaheuristic motivated by the herding schemes of Nomadic Pastoralist (NP). The NP seek optimal herding location for their livestock using some well-defined and robust strategies. UFLP is an NP-hard problem from which many facility location and real-world problems are built around. In this paper, five UFLP datasets were used for the experiments each comprising of five cities and seven, fifteen, thirty, fifty and one hundred cities respectively. The performance of POA was compared and validated with some popular and similar metaheuristic algorithms such as ABC, BBO and PSO. The results obtained proves POA competiveness and superiority in obtaining the lowest allocation cost and convergence rate as the data size increases.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Basu, S., Sharma, M., Ghosh, P.: Metaheuristic applications on discrete facility location problems: a survey, OPSEARCH, pp. 1–32 (2014)

    Google Scholar 

  2. Yinka-Banjo, C., Opesemowo, B.: Metaheuristics for solving facility location optimization problem. Adv. Sci. Technol. Eng. Syst. J. 6, 319–323 (2018)

    Article  Google Scholar 

  3. Shu, W.: A Fast Algorithm for Facility Location Problem, Academy Publisher, pp. 2360–2366 (2013)

    Google Scholar 

  4. Armas, D.J., Juan, A.A., Marques, M.J., Pedroso, J.P.: Solving the deterministic and stochastic uncapacitated facility location problem: from a heuristic to a simheuristic. J. Oper. Res. Soc. 1161–1176 (2017)

    Google Scholar 

  5. Ramos-Figueroa, O., Quiroz-Castellanos, M., Mezura-Montes, E., Schütze, O.: Metaheuristics to solve grouping problems: a review and a case study. Swarm and Evolutionary Computation, pp. 1–69 (2020)

    Google Scholar 

  6. Drezner, Z.: Facility Location: A Survey of Applications and Methods. Springer, New York (1995)

    Book  Google Scholar 

  7. Ghosh, D.: Neighborhood search heuristics for the uncapacitated facility location problem. European J. Oper. Res. 150, 150–162 (2003)

    Article  MathSciNet  Google Scholar 

  8. Lynskey, J., Thar, K.O.T.Z., Hong, C.S.: Facility location problem approach for distributed drones. Symmetry 11(1), 118 (2019)

    Article  Google Scholar 

  9. Monabbati, E.: Uncapacitated facility location problem with self-serving demands. ORION 29(2), 169–200 (2013)

    Article  Google Scholar 

  10. Xu, G., Xu, J.: An improved approximation algorithm for uncapacitated facility location problem with penalty. J. Combinat. Optimizat. 17, 424–436 (2008)

    Article  Google Scholar 

  11. Ulukan, Z., Demircioglu, E.: A survey of discrete facility location problem. Int. J. Soc. Behav. Educ. Econ. Bus. Ind. Eng. 9(7), 2487–2492 (2015)

    Google Scholar 

  12. Yang, X.S.: J. Comput. Eng. Inf. Technol. 1–3 (2013)

    Google Scholar 

  13. Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1, 67–82 (1997)

    Article  Google Scholar 

  14. Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Global Optimizat. 39(3), 459–471 (2007)

    Google Scholar 

  15. Simon, D.: Biogeography-based optimization. IEEE Trans. Evol. Comput. 12(6), 702–713 (2008)

    Article  Google Scholar 

  16. Mirjalili, S.: The ant lion optimizer. Adv. Eng. Software 83, 80–98 (2015)

    Article  Google Scholar 

  17. Mirjalili, S., Lewis, A.: The whale optimization algorithm. Adv. Eng. Software 95, 51–67 (2016)

    Article  Google Scholar 

  18. Yazdani, M., Jolai, F.: Lion Optimization Algorithm (LOA): a nature-inspired metaheuristic algorithm. J. Comput. Des. Eng. 3, 24–36 (2016)

    Google Scholar 

  19. Saremi, S., Mirjalili, S., Lewis, A.: Grasshopper optimisation algorithm: theory and application. Adv. Eng. Softw. 105, 30–47 (2017)

    Article  Google Scholar 

  20. Masadeh, R., Mahafzah, B.A., Sharieh, A.: Sea lion optimization algorithm. (IJACSA) Int. J. Adv. Comput. Sci. Appl. 10(5), 388–395 (2019)

    Google Scholar 

  21. Salawudeen, A.T., Mu’azu, M.B., Sha’aban, Y.A., Adedokun, E.A.: On the development of a novel smell agent optimization (SAO) for optimization problems. In: 2nd International Conference on Information and Communication Technology and its Applications (ICTA 2018), Minna (2018)

    Google Scholar 

  22. Yang, J.-S., Li, S.-X.: An improved grey wolf optimizer based on differential evolution and ellimination mechanism. In: Scientific Reports, pp. 1–21 (2019)

    Google Scholar 

  23. Bozorgi, S.M., Yazdani, S.: IWOA: an improved whale optimization algorithm for optimization problems. J. Comput. Des. Eng. 6(3), 243–259 (2019)

    Google Scholar 

  24. Mafarja, M., Heidari, A.A., Faris, H., Mirjalili, S., Aljarah, I.: Dragonfly algorithm: theory, literature review, and application in feature selection, pp. 47–67. Springer, Cham (2020)

    Google Scholar 

  25. Mafarja, M., Aljarah, I., Heidari, A.A., Faris, H., Fournier-Viger, P., Li, X., Mirjalili, S.: Binary dragonfly optimization for feature selection using time-varying transfer functions. Knowledge-Based Syst. 161, 185–204 (2018)

    Article  Google Scholar 

  26. Mafarja, M., Jarrar, R., Ahmad, S., Abusnaina, A.: Feature selection using binary particle swarm optimization with time varying inertia weight strategies. In: The ACM 2018 in 2nd International Conference on Future Networks & Distributed Systems, Amman, Jordan (2018)

    Google Scholar 

  27. Abdullahi, I.M., Mu’azu, M.B., Olaniyi, O.M., Agajo, J.: Pastoralist optimization algorithm (POA): a novel nature-inspired metaheuristic optimization algorithm. In: International Conference on Global and Emerging Trends (2018), Abuja (2018)

    Google Scholar 

  28. Pratiwi, A.B., Faiza, N., Winarko, E.: Penerapan Cuckoo Search Algorithm (CSA) untuk menyelesaikan uncapacitated facility location problem. Contemporary Math. Appl. 1(1), 34–45 (2019)

    Article  Google Scholar 

  29. Atta, S., Mahapatra, P., Mukhopadhyay, A.: Solving uncapacitated facility location problem using heuristic algorithms. Int. J. Nat. Comput. Res. 8(2), 18–50 (2019)

    Article  Google Scholar 

  30. Atta, S., Mahapatra, P., Mukhopadhyay, A.: Solving uncapacitated facility location problem using monkey algorithm. In: Intelligent Engineering Informatics, Advances in Intelligent Systems and Computing 695, pp. 71–78. Springer, Singapore (2018)

    Google Scholar 

  31. Tsuya, K., Takaya, M., Fazekas, S.Z., Yamamura, A.: Firefly algorithm for uncapacitated facility location problem and number of fireflies. RIMS Kokyuroku 2051(10), 149–157 (2017)

    Google Scholar 

  32. Koc, I.: Big bang-big crunch optimization algorithm for solving the uncapacitated facility location problem. Int. J. Intell. Syst. Appl. Eng. 4(1), 185–189 (2016)

    Article  Google Scholar 

  33. Abdullahi, I.M., Mu’azu, M.B., Olaniyi, O.M., Agajo, J.: An investigative parameter analysis of Pastoralist Optimization Algorithm (Poa): a novel metaheuristic optimization algorithm. J. Sci. Technol. Educ. 7(3), 267–272 (2019)

    Google Scholar 

  34. Abdullahi, I.M., Mu’azu, M.B., Olaniyi, O.M., Agajo, J.: A novel cultural evolution-based nomadic pastoralist optimization algorithm (NPOA): the mathematical models. In: 2nd International Conference of the IEEE Nigeria Computer Chapter (NigeriaComputConf), Zaria (2019)

    Google Scholar 

  35. Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Global Optimization 39(3), 459–471 (2007)

    Article  MathSciNet  Google Scholar 

  36. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks IV (1995)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Federal University of Technology, Minna, Nigeria

    Ibrahim Mohammed Abdullahi, Olayemi Mikail Olaniyi & James Agajo

  2. Department of Computer Engineering, Ahmadu Bello University, Zaria, Nigeria

    Muhammed Bashir Mu’azu

Authors
  1. Ibrahim Mohammed Abdullahi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Muhammed Bashir Mu’azu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Olayemi Mikail Olaniyi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. James Agajo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ibrahim Mohammed Abdullahi .

Editor information

Editors and Affiliations

  1. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Ajith Abraham

  2. Institute for Information Systems, University of Applied Sciences and Arts Northwestern Switzerland, Olten, Solothurn, Switzerland

    Thomas Hanne

  3. Division of Graduate Studies, Tijuana Institute of Technology, Tijuana, Mexico

    Oscar Castillo

  4. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, AL, USA

    Niketa Gandhi

  5. Universidade Federal da Bahia, Salvador, Bahia, Brazil

    Tatiane Nogueira Rios

  6. Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Abdullahi, I.M., Mu’azu, M.B., Olaniyi, O.M., Agajo, J. (2021). Pastoralist Optimization Algorithm (POA): A Culture-Inspired Metaheuristic for Uncapacitated Facility Location Problem (UFLP). In: Abraham, A., Hanne, T., Castillo, O., Gandhi, N., Nogueira Rios, T., Hong, TP. (eds) Hybrid Intelligent Systems. HIS 2020. Advances in Intelligent Systems and Computing, vol 1375. Springer, Cham. https://doi.org/10.1007/978-3-030-73050-5_72

Download citation

Publish with us

Policies and ethics

Search

Navigation