A Computational Intelligence Optimization Algorithm Based on the Behavior of the Social-Spider

  • Erik CuevasEmail author
  • Miguel Cienfuegos
  • Raul Rojas
  • Alfredo Padilla
Part of the Studies in Computational Intelligence book series (SCI, volume 575)


Classical optimization methods often face great difficulties while dealing with several engineering applications. Under such conditions, the use of computational intelligence approaches has been recently extended to address challenging real-world optimization problems. On the other hand, the interesting and exotic collective behavior of social insects have fascinated and attracted researchers for many years. The collaborative swarming behavior observed in these groups provides survival advantages, where insect aggregations of relatively simple and “unintelligent” individuals can accomplish very complex tasks using only limited local information and simple rules of behavior. Swarm intelligence, as a computational intelligence paradigm, models the collective behavior in swarms of insects or animals. Several algorithms arising from such models have been proposed to solve a wide range of complex optimization problems. In this chapter, a novel swarm algorithm called the Social Spider Optimization (SSO) is proposed for solving optimization tasks. The SSO algorithm is based on the simulation of cooperative behavior of social-spiders. In the proposed algorithm, individuals emulate a group of spiders which interact to each other based on the biological laws of the cooperative colony. The algorithm considers two different search agents (spiders): males and females. Depending on gender, each individual is conducted by a set of different evolutionary operators which mimic different cooperative behaviors that are typically found in the colony. In order to illustrate the proficiency and robustness of the proposed approach, it is compared to other well-known evolutionary methods. The comparison examines several standard benchmark functions that are commonly considered within the literature of evolutionary algorithms. The outcome shows a high performance of the proposed method for searching a global optimum with several benchmark functions.


Swarm algorithms Global optimization Bio-inspired algorithms Computational intelligence Evolutionary algorithms Metaheuristics 


  1. 1.
    Aviles, L.: Sex-ratio bias and possible group selection in the social spider Anelosimus eximius. Am. Nat. 128(1), 1–12 (1986)CrossRefGoogle Scholar
  2. 2.
    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
  3. 3.
    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
  4. 4.
    Bonabeau, E.: Social insect colonies as complex adaptive systems. Ecosystems 1, 437–443 (1998)CrossRefGoogle Scholar
  5. 5.
    Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, New York (1999)zbMATHGoogle Scholar
  6. 6.
    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
  7. 7.
    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
  8. 8.
    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
  9. 9.
    Duan, X., Wang, G.G., Kang, X., Niu, Q., Naterer, G., Peng, Q.: Performance study of mode-pursuing sampling method. Eng. Optim. 41(1) (2009)Google Scholar
  10. 10.
    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). doi: 10.1007/s10732-008-9080-4 Google Scholar
  11. 11.
    Gordon, D.: The organization of work in social insect colonies. Complexity 8(1), 43–46 (2003)CrossRefGoogle Scholar
  12. 12.
    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. Insectes Soc. 56(3), 19–331 (2009)CrossRefGoogle Scholar
  13. 13.
    Hossein, A., Hossein-Alavi, A.: Krill herd: a new bio-inspired optimization algorithm. Commun. Nonlinear Sci. Numer. Simul. 17, 4831–4845 (2012)CrossRefzbMATHMathSciNetGoogle Scholar
  14. 14.
    Hölldobler, B., Wilson, E.O.: The Ants. Harvard University Press (1990). ISBN 0-674-04075-9Google Scholar
  15. 15.
    Hölldobler, B., Wilson, E.O.: Journey to the Ants: A Story of Scientific Exploration (1994). ISBN 0-674-48525-4Google Scholar
  16. 16.
    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
  17. 17.
    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
  18. 18.
    Karaboga, D, Akay, B.: A comparative study of artificial bee colony algorithm. Appl. Math. Comput. 214(1), 108–132 (2009). ISSN 0096-3003Google Scholar
  19. 19.
    Kassabalidis, I., El-Sharkawi, M.A., Marks, R.J., Arabshahi, P., Gray, A.A.: Swarm intelligence for routing in communication networks. Global Telecommunications Conference, GLOBECOM’01, 6, IEEE, pp. 3613–3617 (2001)Google Scholar
  20. 20.
    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
  21. 21.
    Krishnanand, K.R., Nayak, S.K., Panigrahi, B.K., Rout, P.K.: Comparative study of five bio-inspired evolutionary optimization techniques. In: Nature & Biologically Inspired Computing, NaBIC, World Congress on, pp.1231–1236 (2009)Google Scholar
  22. 22.
    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. Academic Press, Burlington (2007)Google Scholar
  23. 23.
    Maxence, S.: Social organization of the colonial spider Leucauge sp. in the Neotropics: vertical stratification within colonies. J. Arachnol. 38, 446–451 (2010)CrossRefGoogle Scholar
  24. 24.
    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, NY, USA, pp. 485–492 (2006)Google Scholar
  25. 25.
    Oster, G., Wilson, E.: Caste and ecology in the social insects. Princeton University Press, Princeton (1978)Google Scholar
  26. 26.
    Pasquet, A.: Cooperation and prey capture efficiency in a social spider, Anelosimus eximius (Araneae, Theridiidae). Ethology 90, 121–133 (1991)CrossRefGoogle Scholar
  27. 27.
    Passino, K.M.: Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst. Mag. 22(3), 52–67 (2002)CrossRefMathSciNetGoogle Scholar
  28. 28.
    Rajabioun, R.: Cuckoo optimization algorithm. Appl. Soft Comput. 11, 5508–5518 (2011)CrossRefGoogle Scholar
  29. 29.
    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
  30. 30.
    Rypstra, A.: Prey size, prey perishability and group foraging in a social spider. Oecologia 86(1), 25–30 (1991)Google Scholar
  31. 31.
    Storn, R., Price, K.: Differential evolution—a simple and efficient heuristicfor global optimization over continuous spaces. J. Glob. Optim. 11(4), 341–359 (1995)Google Scholar
  32. 32.
    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
  33. 33.
    Ulbrich, K., Henschel, J.: Intraspecific competition in a social spider. Ecol. Model. 115(2–3), 243–251 (1999)CrossRefGoogle Scholar
  34. 34.
    Vesterstrom, J., Thomsen, R.: A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems. In: Evolutionary Computation, 2004. CEC2004. Congress on 19–23 June, vol. 2, pp. 1980–1987 (2004)Google Scholar
  35. 35.
    Wan-Li, X., Mei-Qing, A.: An efficient and robust artificial bee colony algorithm for numerical optimization. Comput. Oper. Res. 40, 1256–1265 (2013)CrossRefMathSciNetGoogle Scholar
  36. 36.
    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)CrossRefzbMATHGoogle Scholar
  37. 37.
    Wang, H., Sun, H., Li, C., Rahnamayan, S., Jeng-shyang, P.: Diversity enhanced particle swarm optimization with neighborhood. Inf. Sci. 223, 119–135 (2013)CrossRefGoogle Scholar
  38. 38.
    Wilcoxon, F.: Individual comparisons by ranking methods. Biometrics 1, 80–83 (1945)CrossRefGoogle Scholar
  39. 39.
    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, June 1–6, 2008, Hong Kong, China, pp. 2749–2756. IEEE (2008)Google Scholar
  40. 40.
    Yang, X.: Nature-Inspired Metaheuristic Algorithms. Luniver Press, Beckington (2008)Google Scholar
  41. 41.
    Yang, X.S.: Engineering Optimization: An Introduction with Metaheuristic Applications. Wiley, Hoboken (2010) Google Scholar
  42. 42.
    Ying, J., Ke-Cun, Z., Shao-Jian, Q.: A deterministic global optimization algorithm. Appl. Math. Comput. 185(1), 382–387 (2007)CrossRefzbMATHMathSciNetGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Erik Cuevas
    • 1
    Email author
  • Miguel Cienfuegos
    • 1
  • Raul Rojas
    • 2
  • Alfredo Padilla
    • 3
  1. 1.Departamento de ElectrónicaCUCEI, Universidad de GuadalajaraGuadalajaraMexico
  2. 2.Institut Für InformatikFreie Universität BerlinBerlinGermany
  3. 3.Instituto Tecnológico de CelayaCelayaMexico

Personalised recommendations