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Updated: Oct 12, 2025

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Dragonfly Algorithm and Its Hybrids: A Survey on Performance, Objectives and Applications.

Bibi Aamirah Shafaa Emambocus1, Muhammed Basheer Jasser1, Aida Mustapha2

  • 1Department of Computing and Information Systems, School of Engineering and Technology, Sunway University, Petaling Jaya 47500, Selangor, Malaysia.

Sensors (Basel, Switzerland)
|November 27, 2021
PubMed
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This comprehensive survey reviews the dragonfly algorithm (DA), a swarm intelligence method inspired by dragonfly behavior. It details DA

Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Swarm Intelligence

Background:

  • Swarm intelligence (SI) algorithms offer cost-effective, rapid, and robust solutions for optimization problems.
  • The dragonfly algorithm (DA) is a recent SI algorithm inspired by dragonfly swarming behaviors, demonstrating superior performance in various applications compared to other SI and evolutionary algorithms.
  • Existing surveys on DA are limited in scope.

Purpose of the Study:

  • To provide a comprehensive survey of the dragonfly algorithm (DA).
  • To analyze the applications, performance, and hybrid variants of DA.
  • To categorize DA hybrids based on problem type, objectives, and methodologies.

Main Methods:

  • Literature review and analysis of swarm intelligence algorithms.
Keywords:
dragonfly algorithmoptimizationswarm intelligence

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  • Comparative performance evaluation of DA against other SI algorithms.
  • Categorization of DA hybrid variants and their enhancement techniques.

    • Main Results:

    • DA exhibits high performance across diverse applications compared to existing algorithms.
    • Analysis of various DA hybrid methods reveals strategies for performance enhancement.
    • Categorization provides insights into the application-specific adaptations of DA hybrids.

    Conclusions:

    • DA is a promising swarm intelligence algorithm with broad applicability.
    • Hybridization strategies significantly enhance DA's performance for specific optimization tasks.
    • Further research into DA and its variants is warranted to explore its full potential.