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An adaptive ranking moth flame optimizer for feature selection

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  • Yu, Xiaobing
  • Wang, Haoyu
  • Lu, Yangchen

Abstract

Feature selection is to identify informative and concise sub-features from raw datasets, which can be modelled as an optimization issue. An adaptive ranking moth-flame optimization (ARMFO) is developed to solve the problem. The proposed ARMFO algorithm has five improvements: the ranking probability divides moths into better and worse groups; each group performs appropriate position-update equations to enhance the local and global search; a self-adaptive chaotic mutation is used to increase the quality of the best flame; a greedy selection is to maintain better solutions, and the structure of flames is changed. The search ability of the ARMFO algorithm is verified on a test suit, and the algorithm has obtained the best results on twenty-one functions, which accounts for 72.41%. Then, the proposed ARMFO algorithm and seven swarm intelligent algorithms are used for feature selection on fourteen datasets from UCI. The proposed ARMFO algorithm has obtained satisfactory results on 9 datasets compared to its seven rivals.

Suggested Citation

  • Yu, Xiaobing & Wang, Haoyu & Lu, Yangchen, 2024. "An adaptive ranking moth flame optimizer for feature selection," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 219(C), pages 164-184.
    • Handle: RePEc:eee:matcom:v:219:y:2024:i:c:p:164-184
    DOI: 10.1016/j.matcom.2023.12.022
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    References listed on IDEAS

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    1. Yu, Kunjie & Qu, Boyang & Yue, Caitong & Ge, Shilei & Chen, Xu & Liang, Jing, 2019. "A performance-guided JAYA algorithm for parameters identification of photovoltaic cell and module," Applied Energy, Elsevier, vol. 237(C), pages 241-257.
    2. Hou, Guolian & Gong, Linjuan & Hu, Bo & Su, Huilin & Huang, Ting & Huang, Congzhi & Fan, Wei & Zhao, Yuanzhu, 2022. "Application of fast adaptive moth-flame optimization in flexible operation modeling for supercritical unit," Energy, Elsevier, vol. 239(PA).
    3. Chen, Chengcheng & Wang, Xianchang & Yu, Helong & Wang, Mingjing & Chen, Huiling, 2021. "Dealing with multi-modality using synthesis of Moth-flame optimizer with sine cosine mechanisms," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 188(C), pages 291-318.
    4. Elaziz, Mohamed Abd & Ewees, Ahmed A. & Ibrahim, Rehab Ali & Lu, Songfeng, 2020. "Opposition-based moth-flame optimization improved by differential evolution for feature selection," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 168(C), pages 48-75.
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    1. Janani, K. & Mohanrasu, S.S. & Kashkynbayev, Ardak & Rakkiyappan, R., 2025. "Ensemble feature selection via CoCoSo method extended to interval-valued intuitionistic fuzzy environment," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 229(C), pages 50-77.
    2. S., Kavitha & N., Kendra & J., Satheeshkumar & T., Amudha & Manavalan, Balachandran, 2025. "Decision making based ensemble feature selection approach through a new score function in q-rung orthopair hesitant fuzzy environment," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 232(C), pages 362-381.

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