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Dynamic Gaussian bare-bones fruit fly optimizers with abandonment mechanism: method and analysis

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Abstract

The Fruit Fly Optimization Algorithm (FOA) is a recent algorithm inspired by the foraging behavior of fruit fly populations. However, the original FOA easily falls into the local optimum in the process of solving practical problems, and has a high probability of escaping from the optimal solution. In order to improve the global search capability and the quality of solutions, a dynamic step length mechanism, abandonment mechanism and Gaussian bare-bones mechanism are introduced into FOA, termed as BareFOA. Firstly, the random and ambiguous behavior of fruit flies during the olfactory phase is described using the abandonment mechanism. The search range of fruit fly populations is automatically adjusted using an update strategy with dynamic step length. As a result, the convergence speed and convergence accuracy of FOA have been greatly improved. Secondly, the Gaussian bare-bones mechanism that overcomes local optimal constraints is introduced, which greatly improves the global search capability of the FOA. Finally, 30 benchmark functions for CEC2017 and seven engineering optimization problems are experimented with and compared to the best-known solutions reported in the literature. The computational results show that the BareFOA not only significantly achieved the superior results on the benchmark problems than other competitive counterparts, but also can offer better results on the engineering optimization design problems.

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Acknowledgements

This research is supported by National Natural Science Foundation of China (U19A2061, U1809209), Science and Technology Development Project of Jilin Province (20190301024NY), Jilin Provincial Industrial Innovation Special Fund Project (2018C039-3) and Medical and Health Technology Projects of Zhejiang province (2019RC207).

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Authors and Affiliations

  1. College of Information Technology, Jilin Agricultural University, Changchun, 130118, China

    Helong Yu & Wenshu Li

  2. College of Computer Science and Technology, Jilin University, Changchun, 130012, China

    Chengcheng Chen

  3. School of Computer Science, Faculty of Engineering and IT, University of Technology Sydney, Ultimo, Australia

    Jie Liang

  4. Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, China

    Wenyong Gui & Huiling Chen

  5. Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam

    Mingjing Wang

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  1. Helong Yu
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Correspondence to Huiling Chen.

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Yu, H., Li, W., Chen, C. et al. Dynamic Gaussian bare-bones fruit fly optimizers with abandonment mechanism: method and analysis. Engineering with Computers 38 (Suppl 1), 743–771 (2022). https://doi.org/10.1007/s00366-020-01174-w

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