Skip to main content
Book cover

Computational Intelligence Applications in Modeling and Control pp 123–146Cite as

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

Part of the Studies in Computational Intelligence book series (SCI,volume 575)

Abstract

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.

Keywords

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

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-11017-2_6
  • Chapter length: 24 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   149.00
Price excludes VAT (USA)
  • ISBN: 978-3-319-11017-2
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   199.99
Price excludes VAT (USA)
Hardcover Book
USD   219.99
Price excludes VAT (USA)
Learn about institutional subscriptions
Fig. 1
Fig. 2
Fig. 3
Fig. 4

References

  1. Aviles, L.: Sex-ratio bias and possible group selection in the social spider Anelosimus eximius. Am. Nat. 128(1), 1–12 (1986)

    CrossRef  Google Scholar 

  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. Banharnsakun, A., Achalakul, T., Sirinaovakul, B.: The best-so-far selection in artificial bee colony algorithm. Appl. Soft Comput. 11, 2888–2901 (2011)

    CrossRef  Google Scholar 

  4. Bonabeau, E.: Social insect colonies as complex adaptive systems. Ecosystems 1, 437–443 (1998)

    CrossRef  Google Scholar 

  5. Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, New York (1999)

    MATH  Google Scholar 

  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. Chen, D.B., Zhao, C.X.: Particle swarm optimization with adaptive population size and its application. Appl. Soft Comput. 9(1), 39–48 (2009)

    CrossRef  Google Scholar 

  8. Damian, O., Andrade, M., Kasumovic, M.: Dynamic population structure and the evolution of spider mating systems. Adv. Insect Physiol. 41, 65–114 (2011)

    CrossRef  Google Scholar 

  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. 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. Gordon, D.: The organization of work in social insect colonies. Complexity 8(1), 43–46 (2003)

    CrossRef  Google Scholar 

  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)

    CrossRef  Google Scholar 

  13. Hossein, A., Hossein-Alavi, A.: Krill herd: a new bio-inspired optimization algorithm. Commun. Nonlinear Sci. Numer. Simul. 17, 4831–4845 (2012)

    CrossRef  MATH  MathSciNet  Google Scholar 

  14. Hölldobler, B., Wilson, E.O.: The Ants. Harvard University Press (1990). ISBN 0-674-04075-9

    Google Scholar 

  15. Hölldobler, B., Wilson, E.O.: Journey to the Ants: A Story of Scientific Exploration (1994). ISBN 0-674-48525-4

    Google Scholar 

  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)

    CrossRef  Google Scholar 

  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. Karaboga, D, Akay, B.: A comparative study of artificial bee colony algorithm. Appl. Math. Comput. 214(1), 108–132 (2009). ISSN 0096-3003

    Google Scholar 

  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. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the 1995 IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948, December 1995

    Google Scholar 

  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. 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. Maxence, S.: Social organization of the colonial spider Leucauge sp. in the Neotropics: vertical stratification within colonies. J. Arachnol. 38, 446–451 (2010)

    CrossRef  Google Scholar 

  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. Oster, G., Wilson, E.: Caste and ecology in the social insects. Princeton University Press, Princeton (1978)

    Google Scholar 

  26. Pasquet, A.: Cooperation and prey capture efficiency in a social spider, Anelosimus eximius (Araneae, Theridiidae). Ethology 90, 121–133 (1991)

    CrossRef  Google Scholar 

  27. Passino, K.M.: Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst. Mag. 22(3), 52–67 (2002)

    CrossRef  MathSciNet  Google Scholar 

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

    CrossRef  Google Scholar 

  29. Rayor, E.C.: Do social spiders cooperate in predator defense and foraging without a web? Behav. Ecol. Sociobiol. 65(10), 1935–1945 (2011)

    CrossRef  Google Scholar 

  30. Rypstra, A.: Prey size, prey perishability and group foraging in a social spider. Oecologia 86(1), 25–30 (1991)

    Google Scholar 

  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. 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. Ulbrich, K., Henschel, J.: Intraspecific competition in a social spider. Ecol. Model. 115(2–3), 243–251 (1999)

    CrossRef  Google Scholar 

  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. Wan-Li, X., Mei-Qing, A.: An efficient and robust artificial bee colony algorithm for numerical optimization. Comput. Oper. Res. 40, 1256–1265 (2013)

    CrossRef  MathSciNet  Google Scholar 

  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)

    CrossRef  MATH  Google Scholar 

  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)

    CrossRef  Google Scholar 

  38. Wilcoxon, F.: Individual comparisons by ranking methods. Biometrics 1, 80–83 (1945)

    CrossRef  Google Scholar 

  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. Yang, X.: Nature-Inspired Metaheuristic Algorithms. Luniver Press, Beckington (2008)

    Google Scholar 

  41. Yang, X.S.: Engineering Optimization: An Introduction with Metaheuristic Applications. Wiley, Hoboken (2010)

    Google Scholar 

  42. Ying, J., Ke-Cun, Z., Shao-Jian, Q.: A deterministic global optimization algorithm. Appl. Math. Comput. 185(1), 382–387 (2007)

    CrossRef  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Departamento de Electrónica, CUCEI, Universidad de Guadalajara, Guadalajara, Mexico

    Erik Cuevas & Miguel Cienfuegos

  2. Institut Für Informatik, Freie Universität Berlin, Berlin, Germany

    Raul Rojas

  3. Instituto Tecnológico de Celaya, Celaya, Mexico

    Alfredo Padilla

Authors
  1. Erik Cuevas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Miguel Cienfuegos
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Raul Rojas
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Alfredo Padilla
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Erik Cuevas .

Editor information

Editors and Affiliations

  1. Faculty of Computers and Information, Benha University, Benha, Egypt

    Ahmad Taher Azar

  2. Research and Development Centre, Vel Tech University, Chennai, India

    Sundarapandian Vaidyanathan

Appendix A. List of Benchmark Functions

Appendix A. List of Benchmark Functions

See Table 4

Table 4 Test functions used in the experimental study
Full size table

Rights and permissions

Reprints and Permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Cuevas, E., Cienfuegos, M., Rojas, R., Padilla, A. (2015). A Computational Intelligence Optimization Algorithm Based on the Behavior of the Social-Spider. In: Azar, A., Vaidyanathan, S. (eds) Computational Intelligence Applications in Modeling and Control. Studies in Computational Intelligence, vol 575. Springer, Cham. https://doi.org/10.1007/978-3-319-11017-2_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-11017-2_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11016-5

  • Online ISBN: 978-3-319-11017-2

  • eBook Packages: EngineeringEngineering (R0)