Abstract
Aquila optimizer (AO) is a nascent meta-heuristic algorithm that draws its inspiration from the four distinct hunting strategies employed by Aquila in nature.While prior research has demonstrated that AO performs admirably on numerous optimization cases, the algorithm faces significant challenges when confronted with complex multidimensional optimization problems. Specifically, unbalanced exploration and development, inefficiency in identifying optimal solutions, and premature convergence all present significant obstacles to the algorithm’s performance. To address these challenges, this study proposes a novel spiral aquila optimizer based on dynamic Gaussian mutation (SGAO). The algorithm introduces a novel nonlinear control factor and combines it with the spiral search strategy derived from the whale optimization algorithm, thereby accelerating the convergence speed of the algorithm. Additionally, to enhance the probability of escaping local optima in the AO, Gaussian mutant solutions are generated using the positional information of the current new particle and the best particle. Diverging from other improved versions of AO, this research analyzes the motion patterns of traditional AO and purposefully introduces fresh optimization approach, enabling the proposed SGAO to exhibit exceptional performance. In order to validate the efficacy of the proposed method, the newly developed SGAO is subjected to extensive simulation experiments across 39 benchmark problems and five engineering application problems, and is compared against other advanced meta-heuristic algorithms. The experimental results demonstrate that SGAO significantly outperforms traditional AO and other advanced meta-heuristic algorithms, exhibiting exceptional performance and competitiveness.
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Acknowledgements
This work was in part supported by the Key Research and Development Project of Hubei Province (No. 2020BAB114), the Key Project of Science and Technology Research Program of Hubei Educational Committee (No.D20211402), and the Project ofXiangyang Industrial Research Institute of Hubei University of Technology (No. XYYJ2022C04).
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This work is Supported by the Key Research and Development Project of Hubei Province (No. 2023BAB094).
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Firstly, Liang Zeng was responsible for the research design and development of the experimental protocol for this study. Additionally, Liang Zeng carried out data collection and analysis, and interpreted and discussed the results. Finally, Liang Zeng drafted the majority of this manuscript and contributed to its revisions and editing. Secondly, Ming Li was responsible for the execution of the experiments and data collection for this study. Junyang Shi also contributed to the data analysis and wrote the methods section of the manuscript. Thirdly, Junyang Shi was responsible for writing the theoretical background and literature review for this study. Ming Li also contributed to the experimental design and data analysis, and provided important insights and recommendations in the discussion and conclusion sections. Lastly, Shanshan Wang was responsible for creating and editing the figures and formatting and proofreading the manuscript. Additionally, Shanshan Wang provided important viewpoints and recommendations for the discussion and conclusion sections, and contributed to the manuscript revisions and editing. In summary, this study was completed through the collaborative efforts of Liang Zeng, Ming Li, Junyang Shi and Shanshan Wang. Each author made significant contributions to different aspects of the research, driving its successful completion.
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Zeng, L., Li, M., Shi, J. et al. Spiral Aquila Optimizer Based on Dynamic Gaussian Mutation: Applications in Global Optimization and Engineering. Neural Process Lett 55, 11653–11699 (2023). https://doi.org/10.1007/s11063-023-11394-y
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DOI: https://doi.org/10.1007/s11063-023-11394-y