Harris Hawks Optimization (HHO)

HHO is a popular swarm-based, gradient-free optimization algorithm with several active and time-varying phases of exploration and exploitation. This algorithm initially published by the prestigious Journal of Future Generation Computer Systems (FGCS) in 2019, and from the first day, it has gained increasing attention among researchers due to its flexible structure, high performance, and high-quality results. The main logic of the HHO method is designed based on the cooperative behavior and chasing styles of Harris' hawks in nature called "surprise pounce". Currently, there are many suggestions about how to enhance the functionality of HHO, and there are also several enhanced variants of the HHO in the leading Elsevier and IEEE transaction journals. HHO phases

Background Facts

The story behind the idea is so beautiful and simple. Harris hawks can disclose various team chasing patterns based on the dynamic nature of scenarios and escaping patterns of the rabbit. They wait and then attack all together with other hawks from different directions, while the rabbit runs with several zig-zags motions.

Mathematical model and structure

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From the algorithmic behaviour view point, there are several effective features in HHO:

Source codes of HHO algorithm

If you have any question regarding the HHO or you need any help in your research or codes of your modified HHO or any assistant in modeling your problem (objective function) or need any help in writing, idea, plots, or your proposal and manuscript, please simply drop an email  here and I will help you online.

I will always be happy to cooperate with you if you have any new idea or proposal on the HHO algorithm. You can contact me in any time. Let’s enjoy finding the optimal solutions to your real-world problems.

Explore and run HHO interactively