Skip to main content
SpringerLink
Log in

Binary Optimisation with an Urban Pigeon-Inspired Swarm Algorithm

  • Conference paper
  • First Online:
Applied Computer Sciences in Engineering (WEA 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1052))

Included in the following conference series:

Abstract

In this paper we introduce a metaheuristic for optimisation of discrete binary problems, derived from a recently proposed particle swarm algorithm inspired on the foraging behaviour of urban pigeons. The new variant of the algorithm is obtained by mapping the real–valued search space of the original version into a discrete binary–valued encoding. We illustrate the feasibility of the method on several binary benchmark problems and we study the impact of different running parameters such as problem dimension, population size and maximum number of evaluations. The potential of the method and possible extensions for improvement are also discussed.

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 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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. Blanco, A., Chaparro, N., Rojas-Galeano, S.: An urban pigeon-inspired optimiser for unconstrained continuous domains. In: 8th Brazilian Conference on Intelligent Systems (BRACIS). IEEE Xplore Digital Library (2019)

    Google Scholar 

  2. Bolaji, A.L., Babatunde, B.S., Shola, P.B.: Adaptation of binary pigeon-inspired algorithm for solving multidimensional Knapsack problem. In: Pant, M., Ray, K., Sharma, T.K., Rawat, S., Bandyopadhyay, A. (eds.) Soft Computing: Theories and Applications. AISC, vol. 583, pp. 743–751. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-5687-1_66

    Chapter  Google Scholar 

  3. Brabazon, A., Cui, W., O’Neill, M.: The raven roosting optimisation algorithm. Soft Comput. 20(2), 525–545 (2016)

    Article  Google Scholar 

  4. Crawford, B., Soto, R., Astorga, G., García, J., Castro, C., Paredes, F.: Putting continuous metaheuristics to work in binary search spaces. Complexity 2017, 19 (2017)

    Article  MathSciNet  Google Scholar 

  5. Dietterich, T.G.: Ensemble methods in machine learning. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 1–15. Springer, Heidelberg (2000). https://doi.org/10.1007/3-540-45014-9_1

    Chapter  Google Scholar 

  6. Duan, H., Qiao, P.: Pigeon-inspired optimization: a new swarm intelligence optimizer for air robot path planning. Int. J. Intell. Comput. Cybern. 7(1), 24–37 (2014)

    Article  MathSciNet  Google Scholar 

  7. Goel, S.: Pigeon optimization algorithm: a novel approach for solving optimization problems. In: 2014 International Conference on Data Mining and Intelligent Computing (ICDMIC), pp. 1–5. IEEE (2014)

    Google Scholar 

  8. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948. IEEE (1995)

    Google Scholar 

  9. Kennedy, J., Eberhart, R.C.: A discrete binary version of the particle swarm algorithm. In: 1997 IEEE International Conference on Systems, Man, and Cybernetics, vol. 5, pp. 4104–4108. IEEE (1997)

    Google Scholar 

  10. Lamy, J.-B.: Artificial Feeding Birds (AFB): a new metaheuristic inspired by the behavior of pigeons. In: Shandilya, S.K., Shandilya, S., Nagar, A.K. (eds.) Advances in Nature-Inspired Computing and Applications. EICC, pp. 43–60. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-96451-5_3

    Chapter  Google Scholar 

  11. Meng, X.B., Gao, X.Z., Lu, L., Liu, Y., Zhang, H.: A new bio-inspired optimisation algorithm: bird swarm algorithm. J. Exp. Theor. Artif. Intell. 28(4), 673–687 (2016)

    Article  Google Scholar 

  12. Merelo, J.J., Laredo, J.L.J., Castillo, P.A., García-Valdez, J.-M., Rojas-Galeano, S.: Exploring concurrent and stateless evolutionary algorithms. In: Kaufmann, P., Castillo, P.A. (eds.) EvoApplications 2019. LNCS, vol. 11454, pp. 405–412. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-16692-2_27

    Chapter  Google Scholar 

  13. Rothlauf, F.: Representations for Genetic and Evolutionary Algorithms, pp. 9–32. Springer, Heidelberg (2006). https://doi.org/10.1007/3-540-32444-5_2

    Book  Google Scholar 

  14. Spennemann, D.H., Watson, M.J.: Dietary habits of urban pigeons (columba livia) and implications of excreta PH-a review. Eur. J. Ecol. 3(1), 27–41 (2017)

    Article  Google Scholar 

  15. Torabi, S., Safi-Esfahani, F.: Improved Raven Roosting Optimization algorithm (IRRO). Swarm Evol. Comput. 40, 144–154 (2018)

    Article  Google Scholar 

  16. Yang, Z., Liu, K., Fan, J., Guo, Y., Niu, Q., Zhang, J.: A novel binary/real-valued pigeon-inspired optimization for economic/environment unit commitment with renewables and plug-in vehicles. Sci. China Inf. Sci. 62(7), 070213 (2019)

    Article  Google Scholar 

  17. Zambrano-Bigiarini, M., Clerc, M., Rojas, R.: Standard particle swarm optimisation 2011 at CEC-2013: a baseline for future PSO improvements. In: 2013 IEEE Congress on Evolutionary Computation, pp. 2337–2344. IEEE (2013)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Universidad Distrital Francisco José de Caldas, Bogotá, Colombia

    Sergio Rojas-Galeano

Authors
  1. Sergio Rojas-Galeano
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sergio Rojas-Galeano .

Editor information

Editors and Affiliations

  1. Department of Industrial Engineering, Universidad Distrital Francisco José de Caldas, Bogotá, Colombia

    Juan Carlos Figueroa-García

  2. Universidad Antonio Nariño, Bogotá, Colombia

    Mario Duarte-González

  3. Universidad Antonio Nariño, Bogotá, Colombia

    Sebastián Jaramillo-Isaza

  4. Universidad del Rosario, Bogotá, Colombia

    Alvaro David Orjuela-Cañon

  5. Corporación Unificada Nacional CUN, Bogotá, Colombia

    Yesid Díaz-Gutierrez

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Rojas-Galeano, S. (2019). Binary Optimisation with an Urban Pigeon-Inspired Swarm Algorithm. In: Figueroa-García, J., Duarte-González, M., Jaramillo-Isaza, S., Orjuela-Cañon, A., Díaz-Gutierrez, Y. (eds) Applied Computer Sciences in Engineering. WEA 2019. Communications in Computer and Information Science, vol 1052. Springer, Cham. https://doi.org/10.1007/978-3-030-31019-6_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-31019-6_17

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-31018-9

  • Online ISBN: 978-3-030-31019-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation