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Opposition-based moth-flame optimization improved by differential evolution for feature selection

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  • Elaziz, Mohamed Abd
  • Ewees, Ahmed A.
  • Ibrahim, Rehab Ali
  • Lu, Songfeng

Abstract

This paper provides an alternative method for creating an optimal subset from features which in turn represent the whole features through improving the moth-flame optimization (MFO) efficiency in searching for such optimal subset. The improvement is performed by combining the opposition-based learning technique and the differential evolution approach with the MFO. The opposition-based learning is used to generate an optimal initial population to improve the convergence of the MFO; meanwhile, the differential evolution is applied to improve the exploitation ability of the MFO. Therefore, the proposed method noted as OMFODE has the ability to avoid getting stuck in a local optimal value, unlike the traditional MFO algorithm and increase the fast convergence. The performance evaluation of our approach will be through a group of experimental results. In the first one, the proposed method has been tested over several CEC2005 benchmark functions. The second experimental series aims to assess the quality of the proposed method to improve the classification of ten UCI datasets by performing feature selection on such datasets. Another experiment is testing our method for classifying a real dataset, which represents some types of the galaxy images. The experimental results illustrated that the proposed algorithm is superior to the state-of-the-art meta-heuristic algorithms in terms of the performance measures.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:matcom:v:168:y:2020:i:c:p:48-75
    DOI: 10.1016/j.matcom.2019.06.017
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    References listed on IDEAS

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    1. Chen Wang & Lincoln C. Wood & Heng Li & Zhenye Aw & Abolfazl Keshavarzsaleh, 2018. "Applied Artificial Bee Colony Optimization Algorithm in Fire Evacuation Routing System," Journal of Applied Mathematics, Hindawi, vol. 2018, pages 1-17, April.
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    Cited by:

    1. Janani, K. & Mohanrasu, S.S. & Kashkynbayev, Ardak & Rakkiyappan, R., 2024. "Minkowski distance measure in fuzzy PROMETHEE for ensemble feature selection," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 222(C), pages 264-295.
    2. Fan Wang & Xiang Liao & Na Fang & Zhiqiang Jiang, 2022. "Optimal Scheduling of Regional Combined Heat and Power System Based on Improved MFO Algorithm," Energies, MDPI, vol. 15(9), pages 1-30, May.
    3. Kavitha, S. & Satheeshkumar, J. & Amudha, T., 2024. "Multi-label feature selection using q-rung orthopair hesitant fuzzy MCDM approach extended to CODAS," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 222(C), pages 148-173.
    4. 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.
    5. Adil Yousif & Mohammed Bakri Bashir & Awad Ali, 2024. "An Evolutionary Algorithm for Task Clustering and Scheduling in IoT Edge Computing," Mathematics, MDPI, vol. 12(2), pages 1-18, January.
    6. Mohamed Abd Elaziz & Laith Abualigah & Dalia Yousri & Diego Oliva & Mohammed A. A. Al-Qaness & Mohammad H. Nadimi-Shahraki & Ahmed A. Ewees & Songfeng Lu & Rehab Ali Ibrahim, 2021. "Boosting Atomic Orbit Search Using Dynamic-Based Learning for Feature Selection," Mathematics, MDPI, vol. 9(21), pages 1-17, November.
    7. Mohanrasu, S.S. & Janani, K. & Rakkiyappan, R., 2024. "A COPRAS-based Approach to Multi-Label Feature Selection for Text Classification," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 222(C), pages 3-23.
    8. Ahmed A. Ewees & Mohammed A. A. Al-qaness & Laith Abualigah & Diego Oliva & Zakariya Yahya Algamal & Ahmed M. Anter & Rehab Ali Ibrahim & Rania M. Ghoniem & Mohamed Abd Elaziz, 2021. "Boosting Arithmetic Optimization Algorithm with Genetic Algorithm Operators for Feature Selection: Case Study on Cox Proportional Hazards Model," Mathematics, MDPI, vol. 9(18), pages 1-22, September.
    9. Laith Abualigah & Mohamed Abd Elaziz & Dalia Yousri & Mohammed A. A. Al-qaness & Ahmed A. Ewees & Raed Abu Zitar, 2023. "Augmented arithmetic optimization algorithm using opposite-based learning and lévy flight distribution for global optimization and data clustering," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3523-3561, December.

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