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Heuristics are problem-solving strategies that use mental shortcuts to simplify decision-making. Unlike algorithms, which must be followed precisely to achieve a correct result, heuristics offer a general problem-solving framework. They save time and energy but can sometimes lead to less rational decisions.
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Harris Hawk Optimization: A Survey onVariants and Applications.

B K Tripathy1, Praveen Kumar Reddy Maddikunta1, Quoc-Viet Pham2

  • 1School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India.

Computational Intelligence and Neuroscience
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Summary
This summary is machine-generated.

This review surveys the Harris Hawk Optimizer (HHO), detailing its variants and applications in machine learning and engineering. It highlights advancements like fuzzy HHO for future swarm intelligence research.

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Area of Science:

  • Optimization Algorithms
  • Swarm Intelligence
  • Computational Intelligence

Background:

  • The Harris Hawk Optimizer (HHO) is a recent metaheuristic algorithm inspired by the hunting behavior of Harris hawks.
  • It has shown significant success in solving complex optimization problems.
  • A comprehensive literature survey is needed to consolidate its development and applications.

Purpose of the Study:

  • To provide a complete literature survey on the conception and variants of the Harris Hawk Optimizer (HHO).
  • To review updated applications of HHO in established works.
  • To foster deeper understanding and application of HHO, including its advanced forms and real-world problem-solving capabilities.

Main Methods:

  • Literature review of HHO conception, mathematical model, and equation logic.
  • Analysis of various HHO variants from established literature.
  • Review of state-of-the-art improvements, focusing on fuzzy HHO and intuitionistic fuzzy HHO.
  • Examination of HHO applications in machine learning and engineering optimization.

Main Results:

  • A comprehensive overview of the Harris Hawk Optimizer's foundational principles and mathematical framework.
  • Identification and categorization of diverse HHO variants.
  • Detailed review of advanced HHO improvements, including fuzzy logic integrations.
  • Compilation of HHO's successful applications in machine learning and engineering domains.

Conclusions:

  • The Harris Hawk Optimizer is a versatile and successful optimization algorithm with numerous variants and applications.
  • Advanced versions like fuzzy HHO show promise for enhanced performance.
  • This survey provides a foundation for future research in swarm intelligence and real-world HHO applications.