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Water cycle algorithm with adaptive sea and rivers and enhanced position updating strategy for numerical optimization

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Abstract

In this paper, a novel water cycle algorithm is presented by dynamically assigning sea and rivers and devising an enhanced position updating strategy. To effectively maintain the diversity of solutions and ensure the convergence of algorithm, an adaptive distance-based assignment mechanism is first developed to set sea, rivers and their corresponding streams. In this mechanism, the fitness values and position information of solutions are simultaneously considered, and the total number of sea and rivers is nonlinearly reduced during the search process. Meanwhile, an enhanced position updating strategy is designed to update the solutions by incorporating both the gravitational search and greedy strategy. Moreover, a modified evaporation operation is further proposed to dynamically refresh the search capability of algorithm by properly making full use of the promising information of solutions. Differing from the existing WCA variants, the proposed algorithm dynamically assigns sea, rivers and their streams, additionally incorporates the gravitational search and greedy strategy, and fully exploits the obtained promising information in the raining process. Then it could availably strengthen the search effectiveness and balance the exploration and exploitation. Finally, the performance of the proposed algorithm is evaluated by comparing with 12 typical algorithms on 30 CEC2017 benchmark functions. Numerical results show that the proposed algorithm has better performance.

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Acknowledgements

The authors would like to thank the Associate Editor, two anonymous reviewers for their valuable comments and suggestions on the paper. This work is supported by the National Natural Science Foundation of China No. 12101477 and 61273311.

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Authors and Affiliations

  1. School of Science, Xi’an Polytechnic University, Jinhua South Road, Xi’an, 710048, Shaanxi, China

    Mengnan Tian

  2. School of Mathematics and Statistics, Shaanxi Normal University, West Chang’an Avenue, Xi’an, 710062, Shaanxi, China

    Xingbao Gao

  3. School of Computer Science, Shaanxi Normal University, West Chang’an Avenue, Xi’an, 710062, Shaanxi, China

    Xueqing Yan

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  1. Mengnan Tian
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Tian, M., Gao, X. & Yan, X. Water cycle algorithm with adaptive sea and rivers and enhanced position updating strategy for numerical optimization. Neural Comput & Applic 35, 13387–13416 (2023). https://doi.org/10.1007/s00521-023-08365-8

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