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.
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The authors would like to thank the anonymous reviewers for their constructive and helpful comments and suggestions.
Salah Al-Sharhan and Mahamed G. H. Omran have contributed equally to this work.
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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
- Symbiosis organisms search
- Evolutionary algorithms
- Nature-inspired optimization algorithms and metaheuristics