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Dragonfly Algorithm and Its Applications in Applied Science Survey.

Chnoor M Rahman1,2, Tarik A Rashid3

  • 1Technical College of Informatics, Sulaimany Polytechnic University, Sulaimany, Iraq.

Computational Intelligence and Neuroscience
|December 31, 2019
PubMed
Summary
This summary is machine-generated.

The Dragonfly Algorithm, a novel heuristic optimization technique, demonstrates strong exploration capabilities and superior convergence rates compared to other metaheuristic algorithms. This survey provides a comprehensive overview, applications, and future research directions for the Dragonfly Algorithm.

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Area of Science:

  • Optimization Algorithms
  • Heuristic Computing
  • Computational Intelligence

Background:

  • The Dragonfly Algorithm (DA) is a recently developed metaheuristic optimization technique.
  • DA has demonstrated effectiveness in solving various real-world optimization problems.
  • The algorithm possesses three distinct variants and has been explored in hybridized forms.

Purpose of the Study:

  • To provide a comprehensive overview of the Dragonfly Algorithm and its variants.
  • To discuss hybridized versions of the DA.
  • To present applications of the DA in machine learning, image processing, wireless, and networking.

Main Methods:

  • A review of the Dragonfly Algorithm's mechanics and variants.
  • Analysis of hybridization strategies for the DA.
  • Empirical testing on CEC-C06 2019 benchmark functions.
  • Comparative analysis against other metaheuristic algorithms like Particle Swarm Optimization (PSO) and Genetic Algorithms (GA).

Main Results:

  • The Dragonfly Algorithm exhibits excellent exploration capabilities.
  • DA demonstrates a superior convergence rate compared to established algorithms such as PSO and GA.
  • The algorithm's performance is validated on benchmark functions and diverse applied science domains.

Conclusions:

  • The Dragonfly Algorithm is a promising optimization tool with significant potential.
  • Identified strengths and weaknesses of the DA are discussed.
  • Recommendations for future research are provided to enhance the algorithm's performance and address limitations.