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Self-adaptively commensal learning-based Jaya algorithm with multi-populations and its application

Abstract

Jaya algorithm is an advanced optimization algorithm, which has been applied to many real-world optimization problems. Jaya algorithm has better performance in some optimization field. However, Jaya algorithm exploration capability is not better. In order to enhance exploration capability of the Jaya algorithm, a self-adaptively commensal learning-based Jaya algorithm with multi-populations (Jaya-SCLMP) is presented in this paper. In Jaya-SCLMP, a commensal learning strategy is used to increase the probability of finding the global optimum, in which the person history best and worst information is used to explore new solution area. Moreover, a multi-populations strategy based on Gaussian distribution scheme and learning dictionary is utilized to enhance the exploration capability, meanwhile every subpopulation employed three Gaussian distributions at each generation, roulette wheel selection is employed to choose a scheme based on learning dictionary. The performance of Jaya-SCLMP is evaluated based on 28 CEC 2013 unconstrained benchmark problems. In addition, three reliability problems, i.e., complex (bridge) system, series system and series–parallel system, are selected. Compared with several Jaya variants and several state-of-the-art other algorithms, the experimental results reveal that Jaya-SCLMP is effective.

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Data availability

All data used to support the findings of this study are available from our experiments. Upon request, please contact the author to provide them.

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Acknowledgements

The authors are grateful to the editor and the anonymous referees for their constructive comments and recommendations, which have helped to improve this paper significantly. The authors would also like to express their sincere thanks to P. N. Suganthan for the useful information about metaheuristic algorithm and optimization problems on their homepages. We appreciate R.V. Rao for providing the original Jaya algorithm code. This work is supported by Guangzhou Science and Technology Plan Project (201804010299), National Nature Science Foundation of China (Grant No. 61806058), Nature Science Foundation of Guangdong province (2018A030310063).

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Correspondence to Haibin Ouyang.

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Zuanjia Xie and Chunliang Zhang are the common first author.

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Xie, Z., Zhang, C., Ouyang, H. et al. Self-adaptively commensal learning-based Jaya algorithm with multi-populations and its application. Soft Comput 25, 15163–15181 (2021). https://doi.org/10.1007/s00500-021-06445-2

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Keywords

  • Jaya algorithm
  • Multi-populations strategy
  • Learning strategy
  • Reliability problem