Mathematical model and structure
From the algorithmic behaviour view point, there are several effective features in HHO :
- Escaping energy parameter has a dynamic randomized time-varying nature, which can further improve and harmonize the exploratory and exploitive patterns of HHO. This factor also supports HHO to conduct a smooth transition between the exploration and exploitation.
- Different exploration mechanisms with respect to the average location of hawks can increase the exploratory trends of HHO throughout initial iterations.
- Diverse LF-based patterns with short-length jumps enrich the exploitative behaviors of HHO when directing a local search.
- The progressive selection scheme supports search agents to progressively advance their position and only select a better position, which can improve the superiority of solutions and intensification powers of HHO throughout the optimization procedure.
- HHO shows a series of searching strategies and then, it selects the best movement step. This feature has also a constructive influence on the exploitation inclinations of HHO.
- The randomized jump strength can assist candidate solutions in harmonising the exploration and exploitation leanings.
- The application of adaptive and time-varying compoennts allows HHO to handle difficulties of a feature space including local optimal solutions, multi-modality, and deceptive optima.