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Whale optimization algorithm based on dynamic pinhole imaging and adaptive strategy

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Abstract

To solve the problems of premature convergence and easily falling into local optimum, a whale optimization algorithm based on dynamic pinhole imaging and adaptive strategy is proposed in this paper. In the exploitation phase, the dynamic pinhole imaging strategy allows the whale population to approach the optimal solution faster, thereby accelerating the convergence speed of the algorithm. In the exploration phase, adaptive inertial weights based on dynamic boundaries and dimensions can enrich the diversity of the population and balance the algorithm’s exploitation and exploration capabilities. The local mutation mechanism can adjust the search range of the algorithm dynamically. The improved algorithm has been extensively tested in 20 well-known benchmark functions and four complex constrained engineering optimization problems, and compared with the ones of other improved algorithms presented in literatures. The test results show that the improved algorithm has faster convergence speed and higher convergence accuracy and can effectively jump out of the local optimum.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 61603127). The authors would like to thank all the anonymous referees for their valuable comments and suggestions to further improve the quality of this work.

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Authors and Affiliations

  1. Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, School of Electrical and Electronics Engineering, Hubei University of Technology, Wuhan, 430068, China

    Maodong Li, Guanghui Xu, Bo Fu & Xilin Zhao

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  1. Maodong Li
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  2. Guanghui Xu
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  3. Bo Fu
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  4. Xilin Zhao
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Correspondence to Guanghui Xu.

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Li, M., Xu, G., Fu, B. et al. Whale optimization algorithm based on dynamic pinhole imaging and adaptive strategy. J Supercomput 78, 6090–6120 (2022). https://doi.org/10.1007/s11227-021-04116-5

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