Log in

A new binary arithmetic optimization algorithm for uncapacitated facility location problem

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Arithmetic Optimization Algorithm (AOA) is a heuristic method developed in recent years. The original version was developed for continuous optimization problems. Its success in binary optimization problems has not yet been sufficiently tested. In this paper, the binary form of AOA (BinAOA) has been proposed. In addition, the candidate solution production scene of BinAOA is developed with the xor logic gate and the BinAOAX method was proposed. Both methods have been tested for success on well-known uncapacitated facility location problems (UFLPs) in the literature. The UFL problem is a binary optimization problem whose optimum results are known. In this study, the success of BinAOA and BinAOAX on UFLP was demonstrated for the first time. The results of BinAOA and BinAOAX methods were compared and discussed according to best, worst, mean, standard deviation, and gap values. The results of BinAOA and BinAOAX on UFLP are compared with binary heuristic methods used in the literature (TSA, JayaX, ISS, BinSSA, etc.). As a second application, the performances of BinAOA and BinAOAX algorithms are also tested on classical benchmark functions. The binary forms of AOA, AOAX, Jaya, Tree Seed Algorithm (TSA), and Gray Wolf Optimization (GWO) algorithms were compared in different candidate generation scenarios. The results showed that the binary form of AOA is successful and can be preferred as an alternative binary heuristic method.

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

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Germany)

Instant access to the full article PDF.

Algorithm 1
Algorithm 2
Algorithm 3
Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Data availability

In this study, electronic data accessible to everyone was used. Classical benchmark functions are available at https://www.brunel.ac.uk/~mastjjb/jeb/info.html (OR-Library).

References

  1. Baş E, Ülker E (2020) An efficient binary social spider algorithm for feature selection problem. Expert Syst Appl 146:113185

    Google Scholar 

  2. Baş E (2023) Binary aquila optimizer for 0–1 knapsack problems. Eng Appl Artif Intell 118(2023):105592

    Google Scholar 

  3. Beşkirli M, Koc I, Hakli H, Kodaz H (2018) “A new optimization algorithm for solving wind turbine placement problem: binary artificial algae algorithm”, Renew. Energy 121:301–308

    Google Scholar 

  4. Baş E, Ülker E (2020) A binary social spider algorithm for uncapacitated facility location problem. Expert Syst Appl 161(2020):113618

    Google Scholar 

  5. Ghosh D (2003) Neighborhood search heuristics for the uncapacitated facility location problem. Eur J Oper Res 150(1):150–162

    MathSciNet  Google Scholar 

  6. Cornue’Jols G, Nemhauser G, Wolsey L (1983) The uncapicitated facility location problem. Cornell University Operations Research and Industrial Engineering, Technical Report

    Google Scholar 

  7. Aslan M, Gunduz M, Kiran MS (2019) JayaX: jaya algorithm with xor operator for binary optimization. Appl Soft Comput J 82:105576

    Google Scholar 

  8. Cinar AC, Kiran MS (2018) Similarity and logic gate-based tree-seed algorithms for binary optimization. Comput Ind Eng 115:631–646

    Google Scholar 

  9. Saif A, Delage E (2021) Data-driven distributionally robust capacitated facility location problem. Eur J Oper Res 291(3):995–1007

    MathSciNet  Google Scholar 

  10. Zhang F, He Y, Ouyang H, Li W (2023) A fast and efficient discrete evolutionary algorithm for the uncapacitated facility location problem. Expert Syst Appl 213:118978

    Google Scholar 

  11. Hakli H, Ortacay Z (2019) An improved scatter search algorithm for the uncapacitated facility location problem. Comput Ind Eng 135:855–867

    Google Scholar 

  12. Souto G, Morais I, Mauri GR, Ribeiro GM, González PH (2021) A hybrid matheuristic for the two-stage capacitated facility location problem. Expert Syst Appl 185:115501

    Google Scholar 

  13. Souto G, Morais I, Mauri GR, Ribeiro GM, González PH (2021) A hybrid matheuristic for the two-stage capacitated facility location problem. Expert Syst Appl 185:115501

    Google Scholar 

  14. Sonuç E (2021) Binary crow search algorithm for the uncapacitated facility location problem. Neural Comput & Applic 33:14669–14685

    Google Scholar 

  15. Sonuç E, Özcan E (2023) An adaptive parallel evolutionary algorithm for solving the uncapacitated facility location problem. Expert Syst Appl 224:119956

    Google Scholar 

  16. Kaya E (2022) BinGSO: galactic swarm optimization powered by binary artificial algae algorithm for solving uncapacitated facility location problems. Neural Comput & Applic 34:11063–11082

    Google Scholar 

  17. De Armas J, Juan AA, Marquès JM, Pedroso JP (2017) Solving the deterministic and stochastic uncapacitated facility location problem: from a heuristic to a simheuristic. J Opera Res Soc 68(10):1161–1176

    Google Scholar 

  18. Glover, F., Hanafi, S., Guemri, O., Crevits, I., (2017, July). “A simple multi-wave algorithm for the uncapacitated facility location problem,” In: MIC'2017, The 12th edition of the Metaheuristics International Conference (Vol. 5, No. 4, pp 451).

  19. Ramshani M, Ostrowski J, Zhang K, Li X (2019) Two level uncapacitated facility location problem with disruptions. Comput Ind Eng 137:106089

    Google Scholar 

  20. Alidaee B, Wang H (2022) Uncapacitated (Facility) location problem: a hybrid genetic-tabu search approach. IFAC-PapersOnLine 55(10):1619–1624

    Google Scholar 

  21. Chauhan D, Unnikrishnan A, Figliozzi M (2019) Maximum coverage capacitated facility location problem with range constrained drones. Transp Res Part C: Emerg Technol 99:1–18

    Google Scholar 

  22. Sahman MA, Altun AA, Dündar AO (2017) The binary differential search algorithm approach for solving uncapacitated facility location problems. J Comput Theor Nanosci 14(1):670–684

    CAS  Google Scholar 

  23. Mesa A, Castromayor K, Garillos-Manliguez C et al (2018) Cuckoo search via Levy flights applied to uncapacitated facility location problem. J Ind Eng Int 14:585–592

    Google Scholar 

  24. Caramia M, Mari R (2016) A decomposition approach to solve a bilevel capacitated facility location problem with equity constraints. Optim Lett 10:997–1019

    MathSciNet  Google Scholar 

  25. Ho SC (2015) An iterated tabu search heuristic for the single source capacitated facility location problem. Appl Soft Comput 27:169–178

    Google Scholar 

  26. Guo P, Cheng W, Wang Y (2017) Hybrid evolutionary algorithm with extreme machine learning fitness function evaluation for two-stage capacitated facility location problems. Expert Syst Appl 71:57–68

    Google Scholar 

  27. Yang Z, Chen H, Chu F, Wang N (2019) An effective hybrid approach to the two-stage capacitated facility location problem. Eur J Oper Res 275(2):467–480

    MathSciNet  Google Scholar 

  28. Gadegaard S, Klose A, Nielsen L (2018) An improved cut-and-solve algorithm for the single-source capacitated facility location problem. EURO J Comput Optim 6(1):1–27

    MathSciNet  Google Scholar 

  29. Atta, S., Mahapatra, P.R.S., Mukhopadhyay, A., (2018). “Solving uncapacitated facility location problem using monkey algorithm,” In: Intelligent Engineering Informatics: Proceedings of the 6th International Conference on FICTA . Springer Singapore pp 71–78

  30. Atta S, Sinha Mahapatra PR, Mukhopadhyay A (2019) Multi-objective uncapacitated facility location problem with customers’ preferences: Pareto-based and weighted sum GA-based approaches. Soft Comput 23:12347–12362

    Google Scholar 

  31. Alenezy EJ (2020) Solving capacitated facility location problem using lagrangian decomposition and volume algorithm. Adv Oper Res 2020:1–7

    MathSciNet  Google Scholar 

  32. Matos T, Oliveira Ó, Gamboa D (2021) RAMP algorithms for the capacitated facility location problem. Ann Math Artif Intell 89(8–9):799–813

    MathSciNet  Google Scholar 

  33. Abualigah L, Diabat A, Mirjalili S, Elaziz MA, Gandomi AH (2021) The arithmetic optimization algorithm. Comput Methods Appl Mech Eng 376(2021):113609

    ADS  MathSciNet  Google Scholar 

  34. Kaveh A, Hamedani KB (2022) Improved arithmetic optimization algorithm and its application to discrete structural optimization. Structures 35:748–764

    Google Scholar 

  35. Hu G, Zhong J, Du B, Wei G (2022) An enhanced hybrid arithmetic optimization algorithm for engineering applications. Comput Methods Appl Mech Eng 394:114901

    ADS  MathSciNet  Google Scholar 

  36. Li XD, Wang JS, Hao WK et al (2022) Chaotic arithmetic optimization algorithm. Appl Intell 52:16718–16757

    Google Scholar 

  37. Mahajan S, Abualigah L, Pandit AK et al (2022) Hybrid Aquila optimizer with arithmetic optimization algorithm for global optimization tasks. Soft Comput 26:4863–4881

    Google Scholar 

  38. Bhat SJ, Santhosh KV (2022) “A localization and deployment model for wireless sensor networks using arithmetic optimization algorithm”, Peer-to-Peer Netw. Appl 15:1473–1485

    Google Scholar 

  39. Aydemir SB (2023) A novel arithmetic optimization algorithm based on chaotic maps for global optimization. Evol Intel 16:981–996

    Google Scholar 

  40. Issa M (2023) Enhanced arithmetic optimization algorithm for parameter estimation of pid controller. Arab J Sci Eng 48:2191–2205

    PubMed  Google Scholar 

  41. Bansal P, Gehlot K, Singhal A et al (2022) Automatic detection of osteosarcoma based on integrated features and feature selection using binary arithmetic optimization algorithm. Multimed Tools Appl 81:8807–8834

    PubMed  PubMed Central  Google Scholar 

  42. Pashaei E, Pashaei E (2022) Hybrid binary arithmetic optimization algorithm with simulated annealing for feature selection in high-dimensional biomedical data. J Supercomput 78:15598–15637

    Google Scholar 

  43. Baş E (2021) Hybrid the arithmetic optimization algorithm for constrained optimization problems. Konya J Eng Sci 9(3):713–734

    Google Scholar 

  44. Ervural B, Hakli H (2023) A binary reptile search algorithm based on transfer functions with a new stochastic repair method for 0–1 knapsack problems. Comput Indus Eng 178:109080

    Google Scholar 

  45. Mirjalili S, Zhang H, Mirjalili S, Chalup S, Noman N (2020) A Novel U-shaped transfer function for binary particle swarm optimisation. Adv Intell Syst Comput 1138:241–259

    Google Scholar 

  46. Guo S, Wang J, Guo M (2020) Z-shaped transfer functions for binary particle swarm optimization algorithm. Comput Intell Neurosci 2020:6502807

    PubMed  PubMed Central  Google Scholar 

  47. He Y, Zhang F, Mirjalili S, Zhang T (2022) Novel binary differential evolution algorithm based on Taper-shaped transfer functions for binary optimization problems. Swarm Evolut Comput 69:101022

    Google Scholar 

  48. Ghosh KK, Singh PK, Hong J, Geem ZW, Sarkar R (2020) binary social mimic optimization algorithm with X-shaped transfer function for feature selection. IEEE Access 8:97890–97906

    Google Scholar 

  49. Garcı’a J, Crawford B, Soto R, Astorga G (2019) A clustering algorithm applied to the binarization of swarm intelligence continuous metaheuristics. Swarm Evolution Comput 44:646–664

    Google Scholar 

  50. Kashan MH, Kashan AH, Nahavandi N (2013) A novel differential evolution algorithm for binary optimization. Comput Optim Appl 55(2):481–513

    MathSciNet  Google Scholar 

  51. Mirjalili S, Gandomi AH, Mirjalili SZ, Saremii S, Faris H, Mirjalili SM (2017) Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163–191

    Google Scholar 

  52. Kiran MS (2015) The continuous artificial bee colony algorithm for binary optimization. Appl Soft Comput 33:15–23

    Google Scholar 

  53. Kiran MS, Gunduz M (2013) XOR-based artificial bee colony algorithm for binary optimization. Turkish J Electr Eng Comput Sci 21:2307–2328

    Google Scholar 

  54. Korkmaz S, Kiran MS (2018) An artificial algae algorithm with stigmergic behavior for binary optimization. Appl Soft Comput 64:627–640

    Google Scholar 

  55. Rao R (2016) Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int J Ind Eng Comput 7(1):19–34

    Google Scholar 

  56. Cinar, A.C., Iscan, H., Kiran, M.S. “Tree-Seed algorithm for large-scale binary optimization,”In: The 9th International Conference on Advances in Information Technology, Volume 2017.

  57. Mirjalili S, Mirjalili SM, Lewis A (2014) Gray wolf optimizer. Adv Eng Softw 69:46–61

    Google Scholar 

  58. Verter V (2011) Uncapacitated and Capacitated Facility Location Problems. In: Eiselt H, Marianov V (eds) Foundations of Location Analysis. International Series in Operations Research & Management Science, Springer, New York

    Google Scholar 

Download references

Acknowledgements

The Binary AOA algorithm was previously presented at the 3rd HAGIA SOPHIA INTERNATIONALCONFERENCE ON MULTIDISCIPLINARY SCIENTIFIC STUDIES conference and the details of Binary AOA are given in this paper.

Funding

This study was not funded by any institution.

Author information

Authors and Affiliations

Authors

Contributions

EB: Conceptualization, Investigation, Methodology, Software, Writing—review, original draft & editing. GY: Review, original draft & editing.

Corresponding author

Correspondence to Emine Baş.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Baş, E., Yildizdan, G. A new binary arithmetic optimization algorithm for uncapacitated facility location problem. Neural Comput & Applic 36, 4151–4177 (2024). https://doi.org/10.1007/s00521-023-09261-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-023-09261-x

Keywords

Navigation