Skip to main content
SpringerLink
Log in

An enhanced symbiosis organisms search algorithm: an empirical study

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Many nature-inspired optimization algorithms have recently been proposed to solve difficult optimization problems where the mathematical gradient-based approaches could not be used. However, those approaches were often not tested on a proper set of problems. Moreover, statistical tests are sometimes not used to validate the conclusions. Therefore, empirical analyses of such approaches are needed. In this paper, a very recent nature-inspired approach, symbiosis organisms search (SOS), is investigated. A set of unbiased and characteristically different problems are used to study the performance of SOS. In addition, a comparison with some recent optimization methods is conducted. Then, the effect of SOS only parameter, eco_size, is studied, and the use of different random distributions is also explored. Finally, three simple SOS variants are proposed and compared to the original SOS. Conclusions are validated using nonparametric statistical tests.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Cheng M, Prayogo D (2014) Symbiotic organism search: a new metaheuristic optimization algorithm. Comput Struct 139:98–112

    Article  Google Scholar 

  2. Civicioglu P (2013) Backtracking search optimization algorithm for numerical optimization problems. Appl Math Comput 219(15):8121–8144

    MathSciNet  MATH  Google Scholar 

  3. Das S, Suganthan P (2010) Problem definitions and evaluation criteria for CEC 2011 competition on testing evolutionary algorithms on real world optimization problems. Technical Report, Jadavpur University, Nanyang Technological University

  4. Garcia S, Molina D, Lozano M, Herrera F (2009) 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 Heuristics 15(6):617–644

    Article  MATH  Google Scholar 

  5. Jones D (2010) Good practice in (pseudo) random number generation for bioinformatics applications. Technical Report, UCL Bioinformatics Group

  6. Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical Report TR06, Erciyes University, Engineering Faculty, Computer Engineering Department

  7. Kennedy J (1999) Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance. In: Proceedings of IEEE congress on evolutionary computation, Washington DC, USA, pp 1931–1938

  8. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of the IEEE international joint conference on neural networks. IEEE Press, pp. 1942–1948

  9. Matsumoto M, Nishimura T (1998) Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator. ACM Trans Model Comput Simul 8(1):3–30

    Article  MATH  Google Scholar 

  10. Peer E, Van den Bergh F, Engelbrecht A (2003) Using neighborhoods with the guaranteed convergence PSO. In: Swarm intelligence symposium, Piscataway, New Jersey, USA, IEEE Service Center, pp. 235–242

  11. Sandgren E (1990) Non linear integer and discrete programming in mechanical design optimization. J Mech Des 112(2):223–229

    Article  Google Scholar 

  12. Simon D (2013) Evolutionary optimization algorithms. Wiley, New York

    Google Scholar 

  13. Sorensen K (2015) Metaheuristics—the metaphor exposed. Int Trans Oper Res 22(1):3–18

    Article  MathSciNet  MATH  Google Scholar 

  14. Sorensen K, Sevaux M, Glover F (2016) History of metaheuristics. Handbook of heuristics. Springer, New York

    Google Scholar 

  15. Storn R, Price K (1995) Differential evolution—a simple and efficient adaptive scheme for global optimization over continuous spaces. Technical Report TR-95-012, International Computer Science Institute, Berkeley, CA

  16. Suganthan P, Hansen N, Liang J, Deb K, Chen Y, Auger A, Tiwari S (2005) Problem definitions and evaluation criteria for the CEC2005 special session on real-parameter optimization. Technical Report, Nanyang Technology University, Singapore

  17. Tang K, Yao X, Suganthan PN, MacNish C, Chen YP, Chen CM, Yang Z (2008) Benchmark functions for the CEC’2008 special session and competition on large scale global optimization. Technical Report, Nature Inspired Computation and Applications Laboratory, USTC, China

  18. Yang X (2012) Flower pollination algorithm for global optimization. Lect Notes Comput Sci 7445:240–249

    Article  MATH  Google Scholar 

  19. Yang X (2014) Nature-inspired optimization algorithms. Elsevier, Amsterdam

    MATH  Google Scholar 

  20. Zhang J, Sanderson A (2009) JADE: adaptive differential evolution with optional external archive. IEEE Trans Evol Comput 13(5):945–958

    Article  Google Scholar 

  21. Zhan Z, Zhan J, Li Y, Chung H (2009) Adaptive particle swarm optimization. IEEE Trans Syst Man Cybern Part B 39(6):1362–1381

    Article  Google Scholar 

  22. Zhou A, Sun J, Zhang Q (2015) An estimation of distribution algorithm with cheap and expensive local search methods. IEEE Trans Evol Comput 19(6):807–822

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to thank the anonymous reviewers for their constructive and helpful comments and suggestions.

Author information

Author notes

    Authors and Affiliations

    1. Department of Computer Science, Gulf University for Science and Technology, P.O. Box 7207, 32093, Hawally, Kuwait

      Salah Al-Sharhan & Mahamed G. H. Omran

    Authors
    1. Salah Al-Sharhan
      View author publications

      You can also search for this author in PubMed Google Scholar

    2. Mahamed G. H. Omran
      View author publications

      You can also search for this author in PubMed Google Scholar

    Corresponding author

    Correspondence to Salah Al-Sharhan.

    Additional information

    Salah Al-Sharhan and Mahamed G. H. Omran have contributed equally to this work.

    Rights and permissions

    Reprints and permissions

    About this article

    Check for updates. Verify currency and authenticity via CrossMark

    Cite this article

    Al-Sharhan, S., Omran, M.G.H. An enhanced symbiosis organisms search algorithm: an empirical study. Neural Comput & Applic 29, 1025–1043 (2018). https://doi.org/10.1007/s00521-016-2624-x

    Download citation

    • Received:

    • Accepted:

    • Published:

    • Issue Date:

    • DOI: https://doi.org/10.1007/s00521-016-2624-x

    Keywords

    Search

    Navigation