Skip to main content
SpringerLink
Log in

Ambidextrous Socio-Cultural Algorithms

  • Conference paper
  • First Online:
Computational Science and Its Applications – ICCSA 2020 (ICCSA 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12254))

Included in the following conference series:

Abstract

Metaheuristics are a class of algorithms with some intelligence and self-learning capabilities to find solutions to difficult combinatorial problems. Although the promised solutions are not necessarily globally optimal, they are computationally economical. In general, these types of algorithms have been created by imitating intelligent processes and behaviors observed in nature, sociology, psychology and other disciplines. Metaheuristic-based search and optimization is currently widely used for decision making and problem solving in different contexts. The inspiration for metaheuristic algorithms are mainly based on nature’s behaviour or biological behaviour. Designing a good metaheurisitcs is making a proper trade-off between two forces: Exploration and exploitation. It is one of the most basic dilemmas that both individuals and organizations constantly are facing. But there is a little researched branch, which corresponds to the techniques based on the social behavior of people or communities, which are called Social-inspired. In this paper we explain and compare two socio-inspired metaheuristics solving a benchmark combinatorial problem.

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. Ahmadi, S.-A.: Human behavior-based optimization: a novel metaheuristic approach to solve complex optimization problems. Neural Comput. Appl. 28(1), 233–244 (2017)

    Article  Google Scholar 

  2. Ahmadi-Javid, A.: Anarchic society optimization: a human-inspired method. In: 2011 IEEE Congress of Evolutionary Computation (CEC), pp. 2586–2592. IEEE (2011)

    Google Scholar 

  3. Atashpaz-Gargari, E., Lucas, C.: Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: 2007 IEEE Congress on Evolutionary Computation, pp. 4661–4667. IEEE (2007)

    Google Scholar 

  4. Beasley, J.E., Chu, P.C.: A genetic algorithm for the set covering problem. Eur. J. Oper. Res. 94(2), 392–404 (1996)

    Article  Google Scholar 

  5. Blum, C., Roli, A.: Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Comput. Surv. (CSUR) 35(3), 268–308 (2003)

    Article  Google Scholar 

  6. Crawford, B., Soto, R., Astorga, G., Lemus-Romani, J., Misra, S., Rubio, J.-M.: An adaptive intelligent water drops algorithm for set covering problem. In: 2019 19th International Conference on Computational Science and Its Applications (ICCSA), pp. 39–45. IEEE (2019)

    Google Scholar 

  7. Crawford, B., et al.: A binary cat swarm optimization algorithm for the non-unicost set covering problem. Math. Probl. Eng. (2015)

    Google Scholar 

  8. Crawford, B., Soto, R., Cabrera, G., Salas-Fernández, A., Paredes, F.: Using a social media inspired optimization algorithm to solve the set covering problem. In: Meiselwitz, G. (ed.) HCII 2019. LNCS, vol. 11578, pp. 43–52. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-21902-4_4

    Chapter  Google Scholar 

  9. Crawford, B., Soto, R., Cuesta, R., Paredes, F.: Using the bee colony optimization method to solve the weighted set covering problem. In: Stephanidis, C. (ed.) HCI 2014. CCIS, vol. 434, pp. 493–497. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07857-1_86

    Chapter  Google Scholar 

  10. Crawford, B., Soto, R., de la Barra, C.L., Crawford, K., Paredes, F., Johnson, F.: A better understanding of the behaviour of metaheuristics: a psychological view. In: Stephanidis, C. (ed.) HCI 2014. CCIS, vol. 434, pp. 515–518. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07857-1_90

    Chapter  Google Scholar 

  11. Crawford, B., Soto, R., Peña, C., Palma, W., Johnson, F., Paredes, F.: Solving the set covering problem with a shuffled frog leaping algorithm. In: Nguyen, N.T., Trawiński, B., Kosala, R. (eds.) ACIIDS 2015. LNCS (LNAI), vol. 9012, pp. 41–50. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-15705-4_5

    Chapter  Google Scholar 

  12. Crawford, B., Soto, R., Suárez, M.O., Paredes, F., Johnson, F.: Binary firefly algorithm for the set covering problem. In: 2014 9th Iberian Conference on Information Systems and Technologies (CISTI), pp. 1–5. IEEE (2014)

    Google Scholar 

  13. Duncan, R.B.: The ambidextrous organization: designing dual structures for innovation. Manag. Organ. 1(1), 167–188 (1976)

    Google Scholar 

  14. Emami, H., Derakhshan, F.: Election algorithm: a new socio-politically inspired strategy. AI Commun. 28(3), 591–603 (2015)

    Article  MathSciNet  Google Scholar 

  15. Hosseini, S., Al Khaled, A.: A survey on the imperialist competitive algorithm metaheuristic: implementation in engineering domain and directions for future research. Appl. Soft Comput. 24, 1078–1094 (2014)

    Google Scholar 

  16. Huan, T.T., Kulkarni, A.J., Kanesan, J., Huang, C.J., Abraham, A.: Ideology algorithm: a socio-inspired optimization methodology. Neural Comput. Appl. 28(1), 845–876 (2017)

    Article  Google Scholar 

  17. Karp, R.M.: Reducibility among combinatorial problems (1972). https://people.eecs.berkeley.edu/~luca/cs172/karp.pdf

  18. Kashan, A.H.: League championship algorithm: a new algorithm for numerical function optimization. In: 2009 International Conference of Soft Computing and Pattern Recognition, pp. 43–48. IEEE (2009)

    Google Scholar 

  19. Kulkarni, A.J., Durugkar, I.P., Kumar, M.: Cohort intelligence: a self supervised learning behavior. In: 2013 IEEE International Conference on Systems, Man, and Cybernetics, pp. 1396–1400. IEEE (2013)

    Google Scholar 

  20. Kumar, M., Kulkarni, A.J.: Socio-inspired optimization metaheuristics: a review. In: Kulkarni, A.J., Singh, P.K., Satapathy, S.C., Husseinzadeh Kashan, A., Tai, K. (eds.) Socio-cultural Inspired Metaheuristics. SCI, vol. 828, pp. 241–265. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-6569-0_12

    Chapter  Google Scholar 

  21. Kumar, M., Kulkarni, A.J., Satapathy, S.C.: Socio evolution & learning optimization algorithm: a socio-inspired optimization methodology. Future Gen. Comput. Syst. 81, 252–272 (2018)

    Article  Google Scholar 

  22. Kuo, H., Lin, C.: Cultural evolution algorithm for global optimizations and its applications. J. Appl. Res. Technol. 11(4), 510–522 (2013)

    Article  Google Scholar 

  23. Liu, Z.-Z., Chu, D.-H., Song, C., Xue, X., Lu, B.-Y.: Social learning optimization (SLO) algorithm paradigm and its application in QoS-aware cloud service composition. Inf. Sci. 326, 315–333 (2016)

    Article  Google Scholar 

  24. Luque, A.G., Dorado, S.R., de Fátima Vieira Severiano, M., Burillo, F.J.: Fundamentos sociales del comportamiento humano. Editorial UOC (2013)

    Google Scholar 

  25. Lv, W., He, C., Li, D., Cheng, S., Luo, S., Zhang, X.: Election campaign optimization algorithm. Procedia Comput. Sci. 1(1), 1377–1386 (2010)

    Article  Google Scholar 

  26. Lv, W., et al.: Verifying election campaign optimization algorithm by several benchmarking functions. In: Tan, Y., Shi, Y., Tan, K.C. (eds.) ICSI 2010. LNCS, vol. 6146, pp. 582–587. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13498-2_76

    Chapter  Google Scholar 

  27. Lv, Z., Shen, F., Zhao, J., Zhu, T.: A swarm intelligence algorithm inspired by Twitter. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds.) ICONIP 2016. LNCS, vol. 9949, pp. 344–351. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46675-0_38

    Chapter  Google Scholar 

  28. Moosavian, N., Roodsari, B.K., et al.: Soccer league competition algorithm, a new method for solving systems of nonlinear equations. Int. J. Intell. Sci. 4(01), 7 (2013)

    Article  Google Scholar 

  29. Rao, R.V., Savsani, V.J., Vakharia, D.: Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput.-Aided Des. 43(3), 303–315 (2011)

    Article  Google Scholar 

  30. Ray, T., Liew, K.-M.: Society and civilization: an optimization algorithm based on the simulation of social behavior. IEEE Trans. Evol. Comput. 7(4), 386–396 (2003)

    Article  Google Scholar 

  31. Satapathy, S., Naik, A.: Social group optimization (SGO): a new population evolutionary optimization technique. Complex Intell. Syst. 2(3), 173–203 (2016)

    Article  Google Scholar 

  32. Soto, R., Crawford, B., González, F., Vega, E., Castro, C., Paredes, F.: Solving the manufacturing cell design problem using human behavior-based algorithm supported by autonomous search. IEEE Access 7, 132228–132239 (2019)

    Article  Google Scholar 

  33. Soto, R., Crawford, B., Muñoz, A., Johnson, F., Paredes, F.: Pre-processing, repairing and transfer functions can help binary electromagnetism-like algorithms. In: Silhavy, R., Senkerik, R., Oplatkova, Z.K., Prokopova, Z., Silhavy, P. (eds.) Artificial Intelligence Perspectives and Applications. AISC, vol. 347, pp. 89–97. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-18476-0_10

    Chapter  Google Scholar 

  34. Sotoudeh-Anvari, A., Hafezalkotob, A.: A bibliography of metaheuristics-review from 2009 to 2015. Int. J. Knowl.-Based Intell. Eng. Syst. 22(1), 83–95 (2018)

    Google Scholar 

  35. Talbi, E.-G.: Metaheuristics: from Design to Implementation, Chapter 1.3, vol. 74. Wiley, Hoboken (2009)

    Google Scholar 

  36. Tzanetos, A., Fister Jr., I., Dounias, G.: A comprehensive database of nature-inspired algorithms. In: Data in Brief, p. 105792 (2020)

    Google Scholar 

  37. Valdivia, S., et al.: Bridges reinforcement through conversion of tied-arch using crow search algorithm. In: Misra, S., et al. (eds.) ICCSA 2019. LNCS, vol. 11623, pp. 525–535. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-24308-1_42

    Chapter  Google Scholar 

  38. Vásquez, C., et al.: Galactic swarm optimization applied to reinforcement of bridges by conversion in cable-stayed arch. In: Misra, S., et al. (eds.) ICCSA 2019. LNCS, vol. 11623, pp. 108–119. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-24308-1_10

    Chapter  Google Scholar 

  39. Xu, J., Zhang, J.: Exploration-exploitation tradeoffs in metaheuristics: survey and analysis. In: Proceedings of the 33rd Chinese Control Conference, pp. 8633–8638. IEEE (2014)

    Google Scholar 

  40. Yang, X.-S.: Metaheuristic optimization: algorithm analysis and open problems. In: Pardalos, P.M., Rebennack, S. (eds.) SEA 2011. LNCS, vol. 6630, pp. 21–32. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-20662-7_2

    Chapter  Google Scholar 

Download references

Acknowledgements

Broderick Crawford is supported by Grant CONICYT/FONDECYT/REGULAR/1171243, Ricardo Soto is supported by Grant CONICYT/FONDECYT/REGULAR/1190129. José Lemus-Romani is supported by National Agency for Research and Development (ANID)/Scholarship Program/DOCTORADO NACIONAL/2019 - 21191692.

Author information

Authors and Affiliations

  1. Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile

    José Lemus-Romani, Broderick Crawford, Ricardo Soto, Kathleen Crawford & Agustín Salas-Fernández

  2. Universidad de Valparaíso, Valparaíso, Chile

    Gino Astorga & Giancarla Foschino

  3. Covenant University, Ota, Nigeria

    Sanjay Misra

  4. Universidad Diego Portales, Santiago, Chile

    Fernando Paredes

Authors
  1. José Lemus-Romani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Broderick Crawford
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ricardo Soto
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Gino Astorga
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sanjay Misra
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Kathleen Crawford
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Giancarla Foschino
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Agustín Salas-Fernández
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Fernando Paredes
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to José Lemus-Romani .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Potenza, Italy

    Beniamino Murgante

  3. Chair- Center of ICT/ICE, Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Cagliari, Cagliari, Italy

    Chiara Garau

  5. University of Cagliari, Cagliari, Italy

    Ivan Blečić

  6. Clayton School of Information Technology, Monash University, Clayton, VIC, Australia

    David Taniar

  7. Department of Information Science, Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  8. University of Minho, Braga, Portugal

    Ana Maria A. C. Rocha

  9. Polytechnic University of Bari, Bari, Italy

    Eufemia Tarantino

  10. Polytechnic University of Bari, Bari, Italy

    Carmelo Maria Torre

  11. Department of Neurology, University of Massachusetts Medical School, Worcester, MA, USA

    Yeliz Karaca

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Lemus-Romani, J. et al. (2020). Ambidextrous Socio-Cultural Algorithms. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science(), vol 12254. Springer, Cham. https://doi.org/10.1007/978-3-030-58817-5_65

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-58817-5_65

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58816-8

  • Online ISBN: 978-3-030-58817-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation