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Nature-Inspired Optimizers pp 47–67Cite as

Dragonfly Algorithm: Theory, Literature Review, and Application in Feature Selection

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 811))

Abstract

In this chapter, a wrapper-based feature selection algorithm is designed and substantiated based on the binary variant of Dragonfly Algorithm (BDA). DA is a successful, well-established metaheuristic that revealed superior efficacy in dealing with various optimization problems including feature selection. In this chapter we are going first present the inspirations and methamatical modeds of DA in details. Then, the performance of this algorithm is tested on a special type of datasets that contain a huge number of features with low number of samples. This type of datasets makes the optimization process harder, because of the large search space, and the lack of adequate samples to train the model. The experimental results showed the ability of DA to deal with this type of datasets better than other optimizers in the literature. Moreover, an extensive literature review for the DA is provided in this chapter.

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Notes

  1. 1.

    http://www.gems-system.org/.

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Authors and Affiliations

  1. Faculty of Engineering and Technology, Department of Computer Science, Birzeit University, PoBox 14, Birzeit, Palestine

    Majdi Mafarja

  2. School of Surveying and Geospatial Engineering, University of Tehran, Tehran, Iran

    Ali Asghar Heidari

  3. King Abdullah II School for Information Technology, The University of Jordan, Amman, Jordan

    Hossam Faris & Ibrahim Aljarah

  4. Institute of Integrated and Intelligent Systems, Griffith University, Nathan, Brisbane, QLD, 4111, Australia

    Seyedali Mirjalili

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  1. Majdi Mafarja
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  4. Seyedali Mirjalili
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  5. Ibrahim Aljarah
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Correspondence to Seyedali Mirjalili .

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  1. Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, QLD, Australia

    Seyedali Mirjalili

  2. Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, QLD, Australia

    Jin Song Dong

  3. Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, QLD, Australia

    Andrew Lewis

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Mafarja, M., Heidari, A.A., Faris, H., Mirjalili, S., Aljarah, I. (2020). Dragonfly Algorithm: Theory, Literature Review, and Application in Feature Selection. In: Mirjalili, S., Song Dong, J., Lewis, A. (eds) Nature-Inspired Optimizers. Studies in Computational Intelligence, vol 811. Springer, Cham. https://doi.org/10.1007/978-3-030-12127-3_4

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