Improved Harris hawks optimization algorithm based on collaborative quantization

Yan Li; Xiang Zhang; Qian Qian; Jiawen Pan; Yong Feng; Yunfa Fu

doi:10.1117/12.2644611

10 November 2022 Improved Harris hawks optimization algorithm based on collaborative quantization

Yan Li, Xiang Zhang, Qian Qian, Jiawen Pan, Yong Feng, Yunfa Fu

Proceedings Volume 12301, 6th International Conference on Mechatronics and Intelligent Robotics (ICMIR2022); 123010Y (2022) https://doi.org/10.1117/12.2644611
Event: 6th International Conference on Mechatronics and Intelligent Robotics, 2022, Kunming, China

Abstract

Harris hawk optimization algorithm (HHO) is one of the population-based algorithms proposed by in 2019. It has received great attention from researchers. However, HHO algorithm still has some problems, such as the exploitation ability is too large compared with the exploration ability, which leads to low optimization accuracy, slow convergence speed and so on. Therefore, the collaborative strategy and quantization strategy are introduced. With the support of the two strategies, the algorithm can avoid falling into local extremum in the early phase of iteration and improve its optimization accuracy at the late phase of iteration. Through the test of four representative functions, compared with the other three basic algorithms, the proposed algorithm greatly improves the optimization accuracy and convergence speed of the optimal solution, and the ability to find global extremum is greatly improved.

Citation Download Citation

Yan Li, Xiang Zhang, Qian Qian, Jiawen Pan, Yong Feng, and Yunfa Fu "Improved Harris hawks optimization algorithm based on collaborative quantization", Proc. SPIE 12301, 6th International Conference on Mechatronics and Intelligent Robotics (ICMIR2022), 123010Y (10 November 2022); https://doi.org/10.1117/12.2644611

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