The Metaheuristic Algorithm of the Social-Spider

  • Erik Cuevas
  • Daniel Zaldívar
  • Marco Pérez-Cisneros
Part of the Studies in Computational Intelligence book series (SCI, volume 775)


Metaheuristic is a computer science field which emulates the cooperative behavior of natural systems such as insects or animals. Many methods resulting from these models have been suggested to solve several complex optimization problems. In this chapter, a metaheuristic approach known as the Social Spider Optimization (SSO) is analyzed for solving optimization problems. The SSO method considers the simulation of the collective operation of social-spiders. In SSO, candidate solutions represent a set of spiders which interacts among them based on the natural laws of the colony. The algorithm examines two different kinds of search agents (spiders): males and females. According to the gender, each element is conducted by a set of different operations which imitate different behaviors that are commonly observed in the colony.


  1. 1.
    Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press Inc, New York (1999)zbMATHGoogle Scholar
  2. 2.
    Kassabalidis, I., El-Sharkawi, M.A., Marks II, R.J., Arabshahi, P., Gray, A.A.: Swarm intelligence for routing in communication networks. In: Global Telecommunications Conference, GLOBECOM ’01, IEEE, vol. 6, pp. 3613–3617 (2001)Google Scholar
  3. 3.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the 1995 IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948, December 1995Google Scholar
  4. 4.
    Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Technical Report-TR06. Engineering Faculty, Computer Engineering Department, Erciyes University (2005)Google Scholar
  5. 5.
    Passino, K.M.: Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst. Mag. 22(3), 52–67 (2002)CrossRefGoogle Scholar
  6. 6.
    Hossein, A., Hossein-Alavi, A.: Krill herd: a new bio-inspired optimization algorithm. Commun. Nonlinear Sci. Numer. Simul. 17, 4831–4845 (2012)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Yang, X.S: Engineering Optimization: An Introduction with Metaheuristic Applications. Wiley, USA (2010)CrossRefGoogle Scholar
  8. 8.
    Rajabioun, R.: Cuckoo Optimization Algorithm. Appl. Soft Comput. 11, 5508–5518 (2011)CrossRefGoogle Scholar
  9. 9.
    Bonabeau, E.: Social insect colonies as complex adaptive systems. Ecosystems 1, 437–443 (1998)CrossRefGoogle Scholar
  10. 10.
    Wang, Y., Li, B., Weise, T., Wang, J., Yuan, B., Tian, Q.: Self-adaptive learning based particle swarm optimization. Inf. Sci. 181(20), 4515–4538 (2011)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Wan-li, X., Mei-qing, A.: An efficient and robust artificial bee colony algorithm for numerical optimization. Comput. Oper. Res. 40, 1256–1265 (2013)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Wang, H., Sun, H., Li, C., Rahnamayan, S., Jeng-shyang, P.: Diversity enhanced particle swarm optimization with neighborhood. Inf. Sci. 223, 119–135 (2013)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Banharnsakun, A., Achalakul, T., Sirinaovakul, B.: The best-so-far selection in artificial bee colony algorithm. Appl. Soft Comput. 11, 2888–2901 (2011)CrossRefGoogle Scholar
  14. 14.
    Gordon, D.: The organization of work in social insect colonies. Complexity 8(1), 43–46 (2003)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Lubin, T.B.: The evolution of sociality in spiders. In Brockmann, H.J. (ed.) Advances in the Study of Behavior, vol. 37, pp. 83–145 (2007)Google Scholar
  16. 16.
    Uetz, G.W.: Colonial web-building spiders: balancing the costs and benefits of group-living. In: Choe, E.J., Crespi, B. (eds.) The Evolution of Social Behavior in Insects and Arachnids, pp. 458–475. Cambridge University Press, Cambridge (1997)Google Scholar
  17. 17.
    Aviles, L.: Sex-ratio bias and possible group selection in the social spider anelosimus eximius. Am. Nat. 128(1), 1–12 (1986)CrossRefGoogle Scholar
  18. 18.
    Burgess, J.W.: Social spacing strategies in spiders. In: Rovner, P.N. (ed.) Spider Communication: Mechanisms and Ecological Significance, pp. 317–351. Princeton University Press, Princeton (1982)Google Scholar
  19. 19.
    Maxence, S.: Social organization of the colonial spider Leucauge sp. in the neotropics: vertical stratification within colonies. J Arachnology 38, 446–451 (2010)CrossRefGoogle Scholar
  20. 20.
    Yip, E.C., Powers, K.S., Avilés, L.: Cooperative capture of large prey solves scaling challenge faced by spider societies. Proc. Nat. Acad. Sci. U.S.A. 105(33), 11818–11822 (2008)CrossRefGoogle Scholar
  21. 21.
    Oster, G., Wilson, E.: Caste and Ecology in the Social Insects. Princeton University Press, Princeton (1978)Google Scholar
  22. 22.
    Hölldobler, B., Wilson, E.O.: Journey to the Ants: A Story of Scientific Exploration. ISBN 0-674-48525-4 (1994)Google Scholar
  23. 23.
    Hölldobler, B., Wilson, E.O.: The Ants. Harvard University Press, USA. ISBN 0-674-04075-9 (1990)Google Scholar
  24. 24.
    Avilés, L.: Causes and consequences of cooperation and permanent-sociality in spiders. In: Choe, B.C. (ed.) The Evolution of Social Behavior in Insects and Arachnids, pp. 476–498. Cambridge University Press, Cambridge (1997)Google Scholar
  25. 25.
    Rayor, E.C.: Do social spiders cooperate in predator defense and foraging without a web? Behav. Ecol. Sociobiol. 65(10), 1935–1945 (2011)CrossRefGoogle Scholar
  26. 26.
    Gove, R., Hayworth, M., Chhetri, M., Rueppell, O.: Division of labour and social insect colony performance in relation to task and mating number under two alternative response threshold models. Insect. Soc. 56(3), 19–331 (2009)CrossRefGoogle Scholar
  27. 27.
    Rypstra, A.L., Prey Size, R.S.: Prey perishability and group foraging in a social spider. Oecologia 86(1), 25–30 (1991)CrossRefGoogle Scholar
  28. 28.
    Pasquet, A.: Cooperation and prey capture efficiency in a social spider, Anelosimus eximius (Araneae, Theridiidae). Ethology 90, 121–133 (1991)CrossRefGoogle Scholar
  29. 29.
    Ulbrich, K., Henschel, J.: Intraspecific competition in a social spider. Ecol. Model. 115(2–3), 243–251 (1999)CrossRefGoogle Scholar
  30. 30.
    Jones, T., Riechert, S.: Patterns of reproductive success associated with social structure and microclimate in a spider system. Anim. Behav. 76(6), 2011–2019 (2008)CrossRefGoogle Scholar
  31. 31.
    Damian, O., Andrade, M., Kasumovic, M.: Dynamic population structure and the evolution of spider mating systems. Adv. Insect Physiol. 41, 65–114 (2011)CrossRefGoogle Scholar
  32. 32.
    Yang, X.-S.: Nature-Inspired Metaheuristic Algorithms. Luniver Press, Beckington (2008)Google Scholar
  33. 33.
    Chen, D.B., Zhao, C.X.: Particle swarm optimization with adaptive population size and its application. Appl. Soft Comput. 9(1), 39–48 (2009)CrossRefGoogle Scholar
  34. 34.
    Storn, R., Price, K.: Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11(4), 341–359 (1995)MathSciNetCrossRefGoogle Scholar
  35. 35.
    Yang, E., Barton, N.H., Arslan, T., Erdogan, A.T.: A novel shifting balance theory-based approach to optimization of an energy-constrained modulation scheme for wireless sensor networks. In: Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2008, Hong Kong, China, IEEE, pp. 2749–2756, 1–6 June 2008Google Scholar
  36. 36.
    Duan, X., Wang, G.G., Kang, X., Niu, Q., Naterer, G., Peng, Q.: Performance study of mode-pursuing sampling method. Eng. Optim. 41(1), 1–21 (2009)CrossRefGoogle Scholar
  37. 37.
    Vesterstrom, J., Thomsen, R.: A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems. In: Congress on Evolutionary Computation, 2004, CEC 2004, vol. 2, pp. 1980–1987, 19–23 June 2004Google Scholar
  38. 38.
    Mezura-Montes, E., Velázquez-Reyes, J., Coello Coello, C.A.: A comparative study of differential evolution variants for global optimization. In: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation (GECCO ’06), ACM, New York, USA, pp. 485–492 (2006)Google Scholar
  39. 39.
    Karaboga, D., Akay, B.: A comparative study of artificial bee colony algorithm. App. Math. Comput. 214(1), 108–132 (2009). ISSN 0096-3003MathSciNetCrossRefGoogle Scholar
  40. 40.
    Krishnanand, K.R., Nayak, S.K., Panigrahi, B.K., Rout, P.K.: Comparative study of five bio-inspired evolutionary optimization techniques. In: World Congress on Nature & Biologically Inspired Computing, NaBIC, pp. 1231–1236 (2009)Google Scholar
  41. 41.
    Ying, J., Ke-Cun, Z., Shao-Jian, Q.: A deterministic global optimization algorithm. Appl. Math. Comput. 185(1), 382–387 (2007)MathSciNetzbMATHGoogle Scholar
  42. 42.
    Rashedia, E., Nezamabadi-pour, H., Saryazdi, S.: Filter modeling using gravitational search algorithm. Eng. Appl. Artif. Intell. 24(1), 117–122 (2011)CrossRefGoogle Scholar
  43. 43.
    Wilcoxon, F.: Individual comparisons by ranking methods. Biometrics 1, 80–83 (1945)MathSciNetCrossRefGoogle Scholar
  44. 44.
    Garcia, S., Molina, D., Lozano, M., Herrera, F.: 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. J Heurist (2008). Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Erik Cuevas
    • 1
  • Daniel Zaldívar
    • 1
  • Marco Pérez-Cisneros
    • 1
  1. 1.CUCEIUniversidad de GuadalajaraGuadalajaraMexico

Personalised recommendations