Harris Hawk Optimization: A Survey onVariants and Applications

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.