The Basics of Harris Hawks Optimization (HHO)
Harris Hawks Optimization (HHO) is a type of optimization algorithm inspired by the hunting behavior of Harris Hawks in nature. This algorithm is a popular swarm-based, gradient-free optimization algorithm that uses cooperative behavior and chasing styles of Harris Hawks to achieve high-quality results by exploring and exploiting the search space in a flexible and efficient way.
HHO was published in the Journal of Future Generation Computer Systems in 2019 and has been gaining increasing attention among researchers due to its high performance and ability to handle difficulties of a feature space including local optimal solutions, multi-modality, and deceptive optima. There are also several variants of HHO in leading Elsevier and IEEE transaction journals, making it a popular choice for optimization in various fields of research.
The Features of HHO
There are several effective features in HHO that improve its performance compared to other optimization algorithms:
Dynamic Escaping Energy Parameter
The escaping energy parameter in HHO has a dynamic randomized time-varying nature, which helps to improve and harmonize exploratory and exploitative patterns. This parameter supports HHO to conduct a smooth transition between exploration and exploitation.
Exploration Mechanisms
HHO uses different exploration mechanisms with respect to the average location of hawks to increase exploratory trends throughout initial iterations. This helps the algorithm to explore the search space more efficiently.
LF-Based Patterns
Diverse LF-based patterns with short-length jumps enrich exploitative behaviors of HHO when directing a local search. These patterns help the algorithm to find even better solutions in the search space.
Progressive Selection Scheme
The progressive selection scheme in HHO allows search agents to progressively advance their position and only select a better position, which intensifies the superiority of solutions throughout the optimization procedure.
Searching Strategies
HHO shows a series of searching strategies and selects the best movement step, which improves the exploitation inclinations of HHO. This feature has a positive influence on the algorithm's ability to find the best solutions in the search space.
Randomized Jump Strength
The randomized jump strength in HHO can assist candidate solutions in harmonizing the exploration and exploitation leanings, making it easier to find high-quality solutions in the search space.
Adaptive and Time-Varying Components
The application of adaptive and time-varying components in HHO allows the algorithm to handle difficulties of a feature space including local optimal solutions, multi-modality, and deceptive optima. This is a powerful feature that sets HHO apart from other optimization algorithms.
In summary, Harris Hawks Optimization (HHO) is a popular swarm-based, gradient-free optimization algorithm that mimics the cooperative behavior and chasing styles of Harris Hawks in nature to achieve high-performance and high-quality results. With its unique features and ability to handle difficulties in a feature space, HHO is a powerful tool for researchers in various fields to optimize their solutions and achieve better results.
