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.
Similar content being viewed by others
References
Cheng M, Prayogo D (2014) Symbiotic organism search: a new metaheuristic optimization algorithm. Comput Struct 139:98–112
Civicioglu P (2013) Backtracking search optimization algorithm for numerical optimization problems. Appl Math Comput 219(15):8121–8144
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
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
Jones D (2010) Good practice in (pseudo) random number generation for bioinformatics applications. Technical Report, UCL Bioinformatics Group
Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical Report TR06, Erciyes University, Engineering Faculty, Computer Engineering Department
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
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of the IEEE international joint conference on neural networks. IEEE Press, pp. 1942–1948
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
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
Sandgren E (1990) Non linear integer and discrete programming in mechanical design optimization. J Mech Des 112(2):223–229
Simon D (2013) Evolutionary optimization algorithms. Wiley, New York
Sorensen K (2015) Metaheuristics—the metaphor exposed. Int Trans Oper Res 22(1):3–18
Sorensen K, Sevaux M, Glover F (2016) History of metaheuristics. Handbook of heuristics. Springer, New York
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
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
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
Yang X (2012) Flower pollination algorithm for global optimization. Lect Notes Comput Sci 7445:240–249
Yang X (2014) Nature-inspired optimization algorithms. Elsevier, Amsterdam
Zhang J, Sanderson A (2009) JADE: adaptive differential evolution with optional external archive. IEEE Trans Evol Comput 13(5):945–958
Zhan Z, Zhan J, Li Y, Chung H (2009) Adaptive particle swarm optimization. IEEE Trans Syst Man Cybern Part B 39(6):1362–1381
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
Acknowledgments
The authors would like to thank the anonymous reviewers for their constructive and helpful comments and suggestions.
Author information
Authors and Affiliations
Corresponding author
Additional information
Salah Al-Sharhan and Mahamed G. H. Omran have contributed equally to this work.
Rights and permissions
About this article
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
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00521-016-2624-x