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The Selfish Herd Optimizer

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New Advancements in Swarm Algorithms: Operators and Applications

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 160))

Abstract

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.

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References

  1. Kennedy, J., Eberhart, R.: Particle swarm optimization. IEEE Int. Conf. Neural Netw. 4, 1942–1948 (1995)

    Google Scholar 

  2. Karaboga, D., Basturk, B.: On the performance of artificial bee colony (ABC) algorithm. Appl. Soft Comput. J. 8(1), 687–697 (2008)

    Article  Google Scholar 

  3. Yang, X.: Firefly algorithm, Lévy flights and global optimization (2010)

    Google Scholar 

  4. Rajabioun, R.: Cuckoo optimization algorithm. Appl. Soft Comput. J. 11(8), 5508–5518 (2011)

    Article  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  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)

    Article  Google Scholar 

  9. Hamilton, W.D.: Geometry for the selfish herd. J. Theor. Biol. 31(2), 295–311 (1971)

    Article  Google Scholar 

  10. Morrell, L.J., Ruxton, G.D., James, R.: Spatial positioning in the selfish herd. Behav. Ecol. 16–22 (2010)

    Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  14. Alcock, J.: Animal Behavior: An Evolutionary Approach. Sinauer Associates Inc., Sunderland, MA (2001)

    Google Scholar 

  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. Fausto, F., Cuevas, E., Valdivia, A., González, A.: A global optimization algorithm inspired in the behavior of selfish herds. BioSystems 160, 39–55 (2017)

    Article  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  18. Reluga, T.C., Viscido, S.: Simulated evolution of selfish herd behavior. J. Theor. Biol. 234(2), 213–225 (2005)

    Article  MathSciNet  Google Scholar 

  19. Thomas, B.: Evolutionary Algorithms in Theory and Practice. Oxford University Press, Inc. (1996)

    Google Scholar 

  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. 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)

    Article  MathSciNet  Google Scholar 

  22. Yang, X.: Nature-Inspired Metaheuristic Algorithms, 2nd edn. Luniver Press, Beckington, UK (2008)

    Google Scholar 

  23. Karaboga, D., Akay, B.: A comparative study of artificial bee colony algorithm. Appl. Math. Comput. 214(1), 108–132 (2009)

    MathSciNet  MATH  Google Scholar 

  24. Mitchell, M.: An Introduction to Genetic Algorithms. The MIT Press, Cambridge, MA (1996)

    MATH  Google Scholar 

  25. Askarzadeh, A.: A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput. Struct. 169, 1–12 (2016)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  27. Mirjalili, S.: Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl.-Based Syst. 89, 228–249 (2015)

    Article  Google Scholar 

  28. Mirjalili, S.: SCA: a sine cosine algorithm for solving optimization problems. Knowl.-Based Syst. 96, 120–133 (2016)

    Article  Google Scholar 

  29. Ji, Y., Zhang, K., Qu, S.: A deterministic global optimization algorithm. Appl. Math. Comput. 185(1), 382–387 (2007)

    MathSciNet  MATH  Google Scholar 

  30. Rashedi, E., Nezamabadi-pour, H., Saryazdi, S.: GSA: a gravitational search algorithm. Inf. Sci. (Ny) 179(13), 2232–2248 (2009)

    Article  Google Scholar 

  31. Wilcoxon, F.: Individual comparisons by ranking methods Frank Wilcoxon. Biometrics Bull. 1(6), 80–83 (2006)

    Article  Google Scholar 

  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 

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Author information

Authors and Affiliations

  1. CUCEI, Universidad de Guadalajara, Guadalajara, Mexico

    Erik Cuevas

  2. CUCEI, Universidad de Guadalajara, Guadalajara, Mexico

    Fernando Fausto

  3. CUCEI, Universidad de Guadalajara, Guadalajara, Mexico

    Adrián González

Authors
  1. Erik Cuevas
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  2. Fernando Fausto
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  3. Adrián González
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Correspondence to Erik Cuevas .

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Cuevas, E., Fausto, F., González, A. (2020). The Selfish Herd Optimizer. In: New Advancements in Swarm Algorithms: Operators and Applications. Intelligent Systems Reference Library, vol 160. Springer, Cham. https://doi.org/10.1007/978-3-030-16339-6_3

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