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A New Chaotic Whale Optimization Algorithm for Features Selection

Author

Listed:
  • Gehad Ismail Sayed

    (Cairo University)

  • Ashraf Darwish

    (Helwan University)

  • Aboul Ella Hassanien

    (Cairo University)

Abstract

The whale optimization algorithm (WOA) is a novel evolutionary algorithm inspired by the behavior of whales. Similar to other evolutionary algorithms, entrapment in local optima and slow convergence speed are two probable problems it encounters in solving challenging real applications. This paper presents a novel chaotic whale optimization algorithm (CWOA) to overcome these problems where chaotic search is embedded in the searching iterations of WOA. Ten chaotic maps are considered to improve the performance of WOA. Experiments on ten benchmark datasets show the novel CWOA is effective for selecting relevant features with a high classification performance and a small number of features. Additionally the performance of CWOA is compared with WOA and ten other optimization algorithms. The experimental results show that circle chaotic map is the best chaotic map to significantly boost the performance of WOA. Moreover, chaotic with modifications of exploration operators outperform the highest performance.

Suggested Citation

  • Gehad Ismail Sayed & Ashraf Darwish & Aboul Ella Hassanien, 2018. "A New Chaotic Whale Optimization Algorithm for Features Selection," Journal of Classification, Springer;The Classification Society, vol. 35(2), pages 300-344, July.
    • Handle: RePEc:spr:jclass:v:35:y:2018:i:2:d:10.1007_s00357-018-9261-2
    DOI: 10.1007/s00357-018-9261-2
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    References listed on IDEAS

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    1. Alatas, Bilal & Akin, Erhan & Ozer, A. Bedri, 2009. "Chaos embedded particle swarm optimization algorithms," Chaos, Solitons & Fractals, Elsevier, vol. 40(4), pages 1715-1734.
    2. Douglas Steinley & Michael J. Brusco, 2007. "Initializing K-means Batch Clustering: A Critical Evaluation of Several Techniques," Journal of Classification, Springer;The Classification Society, vol. 24(1), pages 99-121, June.
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    Cited by:

    1. Douglas L. Steinley, 2018. "Editorial," Journal of Classification, Springer;The Classification Society, vol. 35(3), pages 391-393, October.
    2. Tsu-Yang Wu & Ankang Shao & Jeng-Shyang Pan, 2023. "CTOA: Toward a Chaotic-Based Tumbleweed Optimization Algorithm," Mathematics, MDPI, vol. 11(10), pages 1-43, May.
    3. Che, Zhongyuan & Peng, Chong & Yue, Chenxiao, 2024. "Optimizing LSTM with multi-strategy improved WOA for robust prediction of high-speed machine tests data," Chaos, Solitons & Fractals, Elsevier, vol. 178(C).

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