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Improved Harris Hawks Optimization algorithm based on quantum correction and Nelder-Mead simplex method


  • Received: 20 February 2022 Revised: 23 April 2022 Accepted: 10 May 2022 Published: 23 May 2022
  • Harris Hawks Optimization (HHO) algorithm is a kind of intelligent algorithm that simulates the predation behavior of hawks. It suffers several shortcomings, such as low calculation accuracy, easy to fall into local optima and difficult to balance exploration and exploitation. In view of the above problems, this paper proposes an improved HHO algorithm named as QC-HHO. Firstly, the initial population is generated by Hénon Chaotic Map to enhance the randomness and ergodicity. Secondly, the quantum correction mechanism is introduced in the local search phase to improve optimization accuracy and population diversity. Thirdly, the Nelder-Mead simplex method is used to improve the search performance and breadth. Fourthly, group communication factors describing the relationship between individuals is taken into consideration. Finally, the energy consumption law is integrated into the renewal process of escape energy factor E and jump distance J to balance exploration and exploitation. The QC-HHO is tested on 10 classical benchmark functions and 30 CEC2014 benchmark functions. The results show that it is superior to original HHO algorithm and other improved HHO algorithms. At the same time, the improved algorithm studied in this paper is applied to gas leakage source localization by wireless sensor networks. The experimental results indicate that the accuracy of position and gas release rate are excellent, which verifies the feasibility for application of QC-HHO in practice.

    Citation: Cheng Zhu, Yong Zhang, Xuhua Pan, Qi Chen, Qingyu Fu. Improved Harris Hawks Optimization algorithm based on quantum correction and Nelder-Mead simplex method[J]. Mathematical Biosciences and Engineering, 2022, 19(8): 7606-7648. doi: 10.3934/mbe.2022358

    Related Papers:

  • Harris Hawks Optimization (HHO) algorithm is a kind of intelligent algorithm that simulates the predation behavior of hawks. It suffers several shortcomings, such as low calculation accuracy, easy to fall into local optima and difficult to balance exploration and exploitation. In view of the above problems, this paper proposes an improved HHO algorithm named as QC-HHO. Firstly, the initial population is generated by Hénon Chaotic Map to enhance the randomness and ergodicity. Secondly, the quantum correction mechanism is introduced in the local search phase to improve optimization accuracy and population diversity. Thirdly, the Nelder-Mead simplex method is used to improve the search performance and breadth. Fourthly, group communication factors describing the relationship between individuals is taken into consideration. Finally, the energy consumption law is integrated into the renewal process of escape energy factor E and jump distance J to balance exploration and exploitation. The QC-HHO is tested on 10 classical benchmark functions and 30 CEC2014 benchmark functions. The results show that it is superior to original HHO algorithm and other improved HHO algorithms. At the same time, the improved algorithm studied in this paper is applied to gas leakage source localization by wireless sensor networks. The experimental results indicate that the accuracy of position and gas release rate are excellent, which verifies the feasibility for application of QC-HHO in practice.



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