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

  • Majdi Mafarja
  • Ali Asghar Heidari
  • Hossam Faris
  • Seyedali MirjaliliEmail author
  • Ibrahim Aljarah
Part of the Studies in Computational Intelligence book series (SCI, volume 811)


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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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Majdi Mafarja
    • 1
  • Ali Asghar Heidari
    • 2
  • Hossam Faris
    • 3
  • Seyedali Mirjalili
    • 4
    Email author
  • Ibrahim Aljarah
    • 3
  1. 1.Faculty of Engineering and Technology, Department of Computer ScienceBirzeit UniversityBirzeitPalestine
  2. 2.School of Surveying and Geospatial EngineeringUniversity of TehranTehranIran
  3. 3.King Abdullah II School for Information TechnologyThe University of JordanAmmanJordan
  4. 4.Institute of Integrated and Intelligent Systems, Griffith University, NathanBrisbaneAustralia

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