Skip to main content

Search Based on Human Behaviors

  • Chapter
  • First Online:
Book cover Search and Optimization by Metaheuristics
  • 3238 Accesses

Abstract

Human being is the most intelligent creature on this planet. This chapter introduces various search metaheuristics that are inspired by various behaviors of human creative problem-solving process.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 49.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 64.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 99.99
Price excludes VAT (USA)
  • Durable hardcover 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. Aickelin U, Burke EK, Li J. An evolutionary squeaky wheel optimisation approach to personnel scheduling. IEEE Trans Evol Comput. 2009;13:433–43.

    Article  Google Scholar 

  2. Ali H, Khan FA. Group counseling optimization for multi-objective functions. In: Proceedings of IEEE congress on evolutionary computation (CEC), Cancun, Mexico, June 2013. p. 705–712.

    Google Scholar 

  3. Atashpaz-Gargari E, Lucas C. Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. Proceedings of IEEE congress on evolutionary computation (CEC), Singapore, September 2007. p. 4661–4666.

    Google Scholar 

  4. Burman R, Chakrabarti S, Das S. Democracy-inspired particle swarm optimizer with the concept of peer groups. Soft Comput. 2016, p. 1–20. doi:10.1007/s00500-015-2007-8.

    Google Scholar 

  5. Chen M-H, Chen S-H, Chang P-C. Imperial competitive algorithm with policy learning for the traveling salesman problem. Soft Comput. 2016, p. 1–13. doi:10.1007/s00500-015-1886-z.

    Google Scholar 

  6. Dai C, Chen W, Zhu Y, Zhang X. Seeker optimization algorithm for optimal reactive power dispatch. IEEE Trans Power Syst. 2009;24(3):1218–31.

    Google Scholar 

  7. Dai C, Zhu Y, Chen W. Seeker optimization algorithm. In: Wang Y, Cheung Y, Liu H, editors. Computational intelligence and security, vol. 4456 of Lecture Notes in Computer Science. Berlin: Springer; 2007. p. 167–176.

    Google Scholar 

  8. Eita MA, Fahmy MM. Group counseling optimization: a novel approach. In: Proceedings of the 29th SGAI international conference on innovative techniquesand applications of artificial intelligence (AI-2009), Cambridge, UK, Dec 2009, p. 195–208.

    Google Scholar 

  9. Eita MA, Fahmy MM. Group counseling optimization. Appl Soft Comput. 2014;22:585–604.

    Article  Google Scholar 

  10. Feng X, Zou R, Yu H. A novel optimization algorithm inspired by the creative thinking process. Soft Comput. 2015;19:2955–72.

    Article  Google Scholar 

  11. Ghorbani N, Babaei E. Exchange market algorithm. Appl Soft Comput. 2014;19:177–87.

    Article  Google Scholar 

  12. Joslin D, Clements DP. Squeaky wheel optimization. J Artif Intell Res. 1999;10:353–73.

    MathSciNet  MATH  Google Scholar 

  13. Kamali HR, Sadegheih A, Vahdat-Zad MA, Khademi-Zare H. Immigrant population search algorithm for solving constrained optimization problems. Appl Artif Intell. 2015;29:243–58.

    Article  Google Scholar 

  14. Kashan AH. League championship algorithm (LCA): an algorithm for global optimization inspired by sport championships. Appl Soft Comput. 2014;16:171–200.

    Google Scholar 

  15. Li J, Parkes AJ, Burke EK. Evolutionary squeaky wheel optimization: a new framework for analysis. Evol Comput. 2011;19(3):405–28.

    Article  Google Scholar 

  16. Lim WH, Isa NAM. Teaching and peer-learning particle swarm optimization. Appl Soft Comput. 2014;18:39–58.

    Article  Google Scholar 

  17. Nazari-Shirkouhi S, Eivazy H, Ghodsi R, Rezaie K, Atashpaz-Gargari E. Solving the integrated product mix-outsourcing problem by a novel meta-heuristic algorithm: imperialist competitive algorithm. Expert Syst Appl. 2010;37(12):7615–26.

    Article  Google Scholar 

  18. Osaba E, Diaz F, Onieva E. A novel meta-heuristic based on soccer concepts to solve routing problems. In: Proceedings of the 15th ACM annual conference on genetic and evolutionary computation (GECCO), Amsterdam, The Netherlands, July 2013. p. 1743–1744.

    Google Scholar 

  19. Osaba E, Diaz F, Onieva E. Golden ball: a novel metaheuristic to solve combinatorial optimization problems based on soccer concepts. Appl Intell. 2014;41(1):145–66.

    Article  Google Scholar 

  20. Rao RV, Patel V. An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems. Int J Ind Eng Comput. 2012;3:535–60.

    Google Scholar 

  21. Rao RV, Savsania VJ, Balic J. Teaching-learning-based optimization algorithm for unconstrained and constrained real-parameter optimization problems. Eng Optim. 2012;44:1447–62.

    Article  Google Scholar 

  22. Rao RV, Savsani VJ, Vakharia DP. Teaching-learning-based optimization: an optimization method for continuous non-linear large scale problems. Inf Sci. 2012;183(1):1–15.

    Article  MathSciNet  Google Scholar 

  23. Shi Y. Brain storm optimization algorithm. In: Advances in swarm intelligence, Vol. 6728 of Lecture Notes in Computer Science. Berlin: Springer; 2011. p. 303–309.

    Google Scholar 

  24. Wang L, Yang R, Ni H, Ye W, Fei M, Pardalos PM. A human learning optimization algorithm and its application to multi-dimensional knapsack problems. Appl Soft Comput. 2015;34:736–43.

    Article  Google Scholar 

  25. Zou F, Wang L, Hei X, Chen D. Teaching-learning-based optimization with learning experience of other learners and its application. Appl Soft Comput. 2015;37:725–36.

    Article  Google Scholar 

  26. Zou F, Wang L, Hei X, Chen D, Jiang Q, Li H. Bare-bones teaching-learning-based optimization. Sci World J. 2014; 2014: 17 pages. Article ID 136920.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ke-Lin Du .

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Du, KL., Swamy, M.N.S. (2016). Search Based on Human Behaviors. In: Search and Optimization by Metaheuristics. Birkhäuser, Cham. https://doi.org/10.1007/978-3-319-41192-7_21

Download citation

Publish with us

Policies and ethics