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Harris Hawks Optimization (HHO)

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Ali Asghar Heidari edited this page Mar 5, 2019 · 1 revision

Harris Hawks Optimization (HHO)

HHO is a population-based, gradient-free optimization algorithm. In 2019, Future Generation Computer Systems (FGCS) has published the HHO algorithm. The main inspiration of HHO is the cooperative behavior and chasing style of Harris’ hawks in nature called "surprise pounce".

Background Facts

Harris hawks can reveal a variety of chasing patterns based on the dynamic nature of scenarios and escaping patterns of the rabbit. see the video at:

Harris's Hawks vs. Jackrabbit, National Geographic

Read this short PDF: HHO brief version: http://evo-ml.com/wp-content/uploads/2019/03/HHO-brief-version.pdf

 

The following features can theoretically assist us in realizing why the proposed HHO can be beneficial in exploring or exploiting the search space of a given optimization problem:
  • Escaping energy parameter has a dynamic randomized time-varying nature, which can further boost the exploration and exploitation patterns of HHO. This factor also requires HHO to perform a smooth transition between exploration and exploitation.
  • Different diversification mechanisms with regard to the average location of hawks can boost the exploratory behavior of HHO in initial iterations.
  • Different LF-based patterns with short-length jumps enhance the exploitative behaviors of HHO when conducting a local search.
  • The progressive selection scheme assists search agents to progressively improve their position and only select a better position, which can improve the quality of solutions and intensification powers of HHO during the course of iterations.
  • HHO utilizes a series of searching strategies based on and parameters and then, it selects the best movement step. This capability has also a constructive impact on the exploitation potential of HHO.
  • The randomized jump strength can assist candidate solutions in balancing the exploration and exploitation tendencies.
  • The use of adaptive and time-varying parameters allows HHO to handle difficulties of a search space including local optimal solutions, multi-modality, and deceptive optima.

Source codes of HHO algorithm

  • Matlab source codes of HHO are publicly available here [DOWNLOAD](http://evo-ml.com/wp-content/uploads/2019/03/HHO-brief-version.pdf)
  • Latex codes of HHO section including the Pseudo-code is publicly available here
  • Visio files of figures in HHO section are publicly available here
  • Latex codes of a brief HHO section is publicly available here
  • Latex codes of a brief HHO section including only the main equations is publicly available here
  • You can download the paper from here
  • If you do not have any access to Sciencedirect, please drop Dr. Ali Asghar Heidari an e-mail here and he will send you the paper.
  • If you have any question regarding the proposed HHO or you need any help in codes of HHO or any assistant in modeling your problem or need any help in preparing your proposal and manuscript, please simply drop an email to first author Dr. Ali Asghar Heidari e-mail here and he will help you online.
We will always be happy to cooperate with you if you have any new idea or proposal on the HHO algorithm. You can contact first author Dr. Ali Asghar Heidari. Let’s enjoy finding the optimal solutions of your real-world problems. 
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