The Selfish Herd Optimizer

  • Erik CuevasEmail author
  • Fernando Fausto
  • Adrián González
Part of the Intelligent Systems Reference Library book series (ISRL, volume 160)


In this chapter, a swarm optimization algorithm called Selfish Herd Optimizer (SHO) is presented. The SHO algorithm’s design is based on the emulation of the widely-observed selfish herd behavior, manifested by individuals living in aggregations while exposed to some kind of predation risk. An interesting trait that distinguish the SHO algorithm from other similar approaches is the division of the entire population of search agents in two opposite groups: the members of a selfish herd (the prey), and a pack of starving predators. These two types of search agents interact with each other in ways that allows to emulate the intriguing interaction between prey and predators that arise from the unique behaviors manifested by the members of the so-called selfish herds. This chapter also presents a series of experiments done with the purpose of comparing the performance of the SHO algorithm against other similar swarm optimization approaches, showing remarkable results.


  1. 1.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. IEEE Int. Conf. Neural Netw. 4, 1942–1948 (1995)Google Scholar
  2. 2.
    Karaboga, D., Basturk, B.: On the performance of artificial bee colony (ABC) algorithm. Appl. Soft Comput. J. 8(1), 687–697 (2008)CrossRefGoogle Scholar
  3. 3.
    Yang, X.: Firefly algorithm, Lévy flights and global optimization (2010)Google Scholar
  4. 4.
    Rajabioun, R.: Cuckoo optimization algorithm. Appl. Soft Comput. J. 11(8), 5508–5518 (2011)CrossRefGoogle Scholar
  5. 5.
    Wang, Y., Li, B., Weise, T., Wang, J., Yuan, B., Tian, Q.: Self-adaptive learning based particle swarm optimization. Inf. Sci. (Ny) 181(20), 4515–4538 (2011)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Xiang, W., An, M.: Computers & operations research an efficient and robust artificial bee colony algorithm for numerical optimization. Comput. Oper. Res. 40(5), 1256–1265 (2013)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Wang, H., Sun, H., Li, C., Rahnamayan, S., Pan, J.: Diversity enhanced particle swarm optimization with neighborhood search. Inf. Sci. (Ny) 223, 119–135 (2013)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Banharnsakun, A., Achalakul, T., Sirinaovakul, B.: The best-so-far selection in artificial bee colony algorithm. Appl. Soft Comput. J. 11(2), 2888–2901 (2011)CrossRefGoogle Scholar
  9. 9.
    Hamilton, W.D.: Geometry for the selfish herd. J. Theor. Biol. 31(2), 295–311 (1971)CrossRefGoogle Scholar
  10. 10.
    Morrell, L.J., Ruxton, G.D., James, R.: Spatial positioning in the selfish herd. Behav. Ecol. 16–22 (2010)Google Scholar
  11. 11.
    Eshel, I., Sansone, E., Shaked, A.: On the evolution of group-escape strategies of selfish prey. Theor. Popul. Biol. 80(2), 150–157 (2011)CrossRefGoogle Scholar
  12. 12.
    Viscido, S.V., Wethey, D.S.: Quantitative analysis of fiddler crab flock movement: evidence for ‘selfish herd’ behaviour. Anim. Behav. 63(4), 735–741 (2002)CrossRefGoogle Scholar
  13. 13.
    Orpwood, J.E., Magurran, A.E., Armstrong, J.D., Griffiths, S.W.: Minnows and the selfish herd: effects of predation risk on shoaling behaviour are dependent on habitat complexity. Anim. Behav. 76(1), 143–152 (2008)CrossRefGoogle Scholar
  14. 14.
    Alcock, J.: Animal Behavior: An Evolutionary Approach. Sinauer Associates Inc., Sunderland, MA (2001)Google Scholar
  15. 15.
    Mcclure, M., Despland, E.: Collective foraging patterns of field colonies of Malacosoma disstria caterpillars. Entomol. Soc. Canada2 142(5), 473–480 (2010)Google Scholar
  16. 16.
    Fausto, F., Cuevas, E., Valdivia, A., González, A.: A global optimization algorithm inspired in the behavior of selfish herds. BioSystems 160, 39–55 (2017)CrossRefGoogle Scholar
  17. 17.
    Viscido, S.V., Miller, M., Wethey, D.S.: The dilemma of the selfish herd: the search for a realistic movement rule. J. Theor. Biol. 217(2), 183–194 (2002)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Reluga, T.C., Viscido, S.: Simulated evolution of selfish herd behavior. J. Theor. Biol. 234(2), 213–225 (2005)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Thomas, B.: Evolutionary Algorithms in Theory and Practice. Oxford University Press, Inc. (1996)Google Scholar
  20. 20.
    Voltera, V.: Variations and fluctuations of the number of individuals in animal species libing together. In: Chapman, R.N. (ed.) Animal Ecology. McGraw-Hill (1931)Google Scholar
  21. 21.
    Storn, R., Price, K.: Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11(4), 341–359 (1997)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Yang, X.: Nature-Inspired Metaheuristic Algorithms, 2nd edn. Luniver Press, Beckington, UK (2008)Google Scholar
  23. 23.
    Karaboga, D., Akay, B.: A comparative study of artificial bee colony algorithm. Appl. Math. Comput. 214(1), 108–132 (2009)MathSciNetzbMATHGoogle Scholar
  24. 24.
    Mitchell, M.: An Introduction to Genetic Algorithms. The MIT Press, Cambridge, MA (1996)zbMATHGoogle Scholar
  25. 25.
    Askarzadeh, A.: A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput. Struct. 169, 1–12 (2016)CrossRefGoogle Scholar
  26. 26.
    Mirjalili, S.: Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput. Appl. 27(4), 1053–1073 (2015)CrossRefGoogle Scholar
  27. 27.
    Mirjalili, S.: Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl.-Based Syst. 89, 228–249 (2015)CrossRefGoogle Scholar
  28. 28.
    Mirjalili, S.: SCA: a sine cosine algorithm for solving optimization problems. Knowl.-Based Syst. 96, 120–133 (2016)CrossRefGoogle Scholar
  29. 29.
    Ji, Y., Zhang, K., Qu, S.: A deterministic global optimization algorithm. Appl. Math. Comput. 185(1), 382–387 (2007)MathSciNetzbMATHGoogle Scholar
  30. 30.
    Rashedi, E., Nezamabadi-pour, H., Saryazdi, S.: GSA: a gravitational search algorithm. Inf. Sci. (Ny) 179(13), 2232–2248 (2009)CrossRefGoogle Scholar
  31. 31.
    Wilcoxon, F.: Individual comparisons by ranking methods Frank Wilcoxon. Biometrics Bull. 1(6), 80–83 (2006)CrossRefGoogle Scholar
  32. 32.
    García, S., Molina, D., Lozano, M.: A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour : a case study on the CEC’2005 special session on real parameter optimization. 617–644 (2009)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Erik Cuevas
    • 1
    Email author
  • Fernando Fausto
    • 2
  • Adrián González
    • 3
  1. 1.CUCEI, Universidad de GuadalajaraGuadalajaraMexico
  2. 2.CUCEI, Universidad de GuadalajaraGuadalajaraMexico
  3. 3.CUCEI, Universidad de GuadalajaraGuadalajaraMexico

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