Harris Hawks Optimization

The Harris hawk optimizer is a recent population-based metaheuristics algorithm that simulates the hunting behavior of hawks.
This swarm-based optimizer performs the optimization procedure using a novel way of exploration and exploitation and the multiphases of search.

A review research focuses on the applications and developments of the recent well-established robust optimizer Harris hawk optimizer (HHO) as one of the most popular swarm-based techniques. Moreover, several experiments were carried out to prove the powerfulness and effectiveness of HHO compared with nine other state-of-art algorithms.

The Harris hawks optimization is a recently developed algorithm that simulates Harris hawks’ special hunting behavior known as “seven kills”. The HHO algorithm has some unique features compared with other popular swarm-based optimization methods.
The first is that this optimizer utilizes a time-varying rule which evolves by more iterations of the method during exploration and exploitation. Such a way of shifting from exploration to exploitation propensities can make it subsequently flexible when the optimizer is in front of an undesirable difficulty in the feature space.
Another advantage is that the algorithm has a progressive trend during the convergence process and when shifting from the first diversification / exploration phase to the intensification / exploitation core.
The quality of results in HHO is relatively higher compared with other popular methods, and this feature also supported the widespread applications of this solver.
Moreover, the exploitation phase of this method is compelling based on a greedy manner in picking the best solutions explored so far and ignoring low-quality solutions obtained until that iteration.  

HHO is similar to all metaheurstics algorithms. HHO has many benefits (advantages) and a smaller number of disadvantages. HHO advantages can be listed as follows:

• Good convergence speed.
• Powerful neighborhood search characteristic.
• Good balance between exploration and exploitation.
• Suitable for many kinds of problems.
• Easy to implement.
• Adaptability, scalability, flexibility, and robustness.

The disadvantages of HHO, as with all other algorithms, is that it may stick in local optima, and there is no theatrical converging study frame.