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A New Chaotic Whale Optimization Algorithm for Features Selection

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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.

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Authors and Affiliations

  1. Faculty of Computers and Information, Cairo University, 5 Ahmed Zewail, Ad Doqi, Giza Governorate, 12613, Egypt

    Gehad Ismail Sayed

  2. Faculty of Science, Helwan University, Helwan, Egypt

    Ashraf Darwish

  3. Faculty of Computers and Information, Cairo University, Cairo, Egypt

    Aboul Ella Hassanien

Authors
  1. Gehad Ismail Sayed
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  2. Ashraf Darwish
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  3. Aboul Ella Hassanien
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Correspondence to Gehad Ismail Sayed.

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Sayed, G.I., Darwish, A. & Hassanien, A.E. A New Chaotic Whale Optimization Algorithm for Features Selection. J Classif 35, 300–344 (2018). https://doi.org/10.1007/s00357-018-9261-2

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