Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (4): 1186-1193.DOI: 10.11772/j.issn.1001-9081.2021071244

• The 36 CCF National Conference of Computer Applications (CCF NCCA 2020) • Previous Articles    

Harris hawks optimization algorithm based on chemotaxis correction

Cheng ZHU, Xuhua PAN(), Yong ZHANG   

  1. School of Information Engineering,Tianjin University of Commerce,Tianjin 300134,China
  • Received:2021-07-16 Revised:2021-09-02 Accepted:2021-09-03 Online:2021-09-14 Published:2022-04-10
  • Contact: Xuhua PAN
  • About author:ZHU Cheng, born in 1981, M. S., experimentalist. His research interests include embedded system development, swarm intelligence optimization, image processing.
    ZHANG Yong, born in 1978, Ph. D., associate professor. His research interests include intelligent detection and information processing, sensor network information processing.
  • Supported by:
    Tianjin Natural Science Foundation(20JCYBJC00320)


朱诚, 潘旭华(), 张勇   

  1. 天津商业大学 信息工程学院,天津 300134
  • 通讯作者: 潘旭华
  • 作者简介:朱诚(1981—),男,山东蓬莱人,实验师,硕士,主要研究方向:嵌入式系统、群智能优化、图像处理
  • 基金资助:


Focused on the disadvantages of slow convergence and easy to fall into local optimum of Harris Hawks Optimization (HHO) algorithm, an improved HHO algorithm called Chemotaxis Correction HHO (CC-HHO) algorithm was proposed. Firstly, the state of convergence curve was identified by calculating the rate of decline and change weight of the optimal solution. Secondly, the CC mechanism of the Bacterial Foraging Optimization (BFO) algorithm was introduced into the local search stage to improve the accuracy of optimization. Thirdly, the law of energy consumption was integrated into the updating process of the escape energy factor and the jump distance to balance the exploration and exploitation. Fourthly, elite selection for different combinations of optimal solution and sub-optimal solution was used to improve the universality of global search of the algorithm. Finally, when the search was falling into local optimum, the escape energy was disturbed to realize the forced jumping out. The performance of the improved algorithm was tested by ten benchmark functions. The results show that the search accuracy of CC-HHO algorithm on unimodal functions is better than those of Gravitational Search Algorithm (GSA), Particle Swarm Optimization (PSO) algorithm, Whale Optimization Algorithm (WOA) and other four improved HHO algorithms for more than ten orders of magnitude; there is also more than one order of magnitude superiority on multimodal functions; on the premise that search stability is improved by more than 10% on average, the proposed algorithm has faster convergence speed significantly than the above-mentioned several comparative optimization algorithms with more obvious convergence trend. Experimental results show that CC-HHO algorithm effectively improves the efficiency and robustness of the original algorithm.

Key words: swarm intelligence optimization, Harris Hawks Optimization (HHO), Chemotaxis Correction (CC), elite selection, escape energy factor



关键词: 群智能优化, 哈里斯鹰优化, 趋化校正, 精英选择, 逃逸能量因子

CLC Number: