Study on an Improved Harris Hawks Optimization Algorithm for Control Parameter Tuning

Jie Zhu; Xiuyang Huang; Huachen Ding

doi:10.47297/taposatWSP2633-456915.20260703

PDF
Export
Share
Collection
Album

Study on an Improved Harris Hawks Optimization Algorithm for Control Parameter Tuning

Volume 7, Issue 3

Views: 0 Downloads: 3

Study on an Improved Harris Hawks Optimization Algorithm for Control Parameter Tuning
Study on an Improved Harris Hawks Optimization Algorithm for Control Parameter Tuning
University of Shanghai for Science and Technology, Shanghai, 200093, China
Vol. 7, Issue 3, Pages: 73-78(2026)
Published：30 March 2026
DOI：10.47297/taposatWSP2633-456915.20260703
Accepted：

Scan QR Code

Zhu Jie,Huang Xiuyang,Ding Huachen.Study on an Improved Harris Hawks Optimization Algorithm for Control Parameter Tuning[J].Theory and Practice of Science and Technology,2026,07(03):73-78.
DOI：

Zhu Jie,Huang Xiuyang,Ding Huachen.Study on an Improved Harris Hawks Optimization Algorithm for Control Parameter Tuning[J].Theory and Practice of Science and Technology,2026,07(03):73-78. DOI： 10.47297/taposatWSP2633-456915.20260703.

摘要

Abstract

To address the characteristics of control parameter tuning problems

such as narrow feasible regions

strong boundary sensitivity

significant objective-function nonlinearity

and high requirements for late-stage high-precision search

this paper proposes an improved Harris Hawks Optimization (HHO) algorithm for engineering-oriented tuning tasks. Built upon the standard HHO framework

the proposed method introduces three major improvements: initialization enhancement

stagnation recovery

and late-stage refinement. First

a hybrid initialization strategy that integrates global random sampling

prior-region guidance

and opposition-based learning is adopted to improve the coverage of the initial population over narrow feasible regions and boundary-adjacent high-quality areas. Second

a stagnation-awareness mechanism is constructed based on both the improvement magnitude of the best fitness and population diversity

and a directed escape strategy is incorporated to restore search activity in the middle stage of evolution. Finally

an elite-guided local refinement mechanism is introduced

where coordinate-diagonal hybrid search and random perturbation polishing are employed to enhance final solution accuracy and result stability. The proposed algorithm is validated on two representative control tuning problems

namely LLC and AVR

and its generalization capability is further examined on the supplementary THIRD and MMS scenarios. The results show that the improved HHO achieves better median performance and lower tail risk in both LLC and AVR cases. It is particularly suitable for control parameter tuning problems characterized by narrow feasible regions

pronounced boundary high-quality solutions

and strong demands for terminal refinement.

关键词

Keywords

references

Heidari, A. A.; Mirjalili, S.; Faris, H.; Aljarah, I.; Mafarja, M.; Chen, H. Harris hawks optimization: Algorithm and applications. Future Generation Computer Systems , 2019, 97: 849–872.

Kennedy, J.; Eberhart, R. Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks (ICNN'95) , 1995, Vol. 4 , pp. 1942 – 1948 .

Mirjalili, S.; Mirjalili, S. M.; Lewis, A. Grey Wolf Optimizer. Advances in Engineering Software , 2014, 69: 46–61.

Tizhoosh, H. R. Opposition-Based Learning: A New Scheme for Machine Intelligence. In: Proceedings of CIMCA 2005 , 2005, Vol. 1 , pp. 695 – 701 .

Viswanathan, G. M.; Afanasyev, V.; Buldyrev, S. V.; Havlin, S.; da Luz, M. G. E.; Raposo, E. P.; Stanley, H. E. Lévy flights in random searches. Physica A: Statistical Mechanics and its Applications , 2000, 282(1): 1–12.

Ahi, B.; Haeri, M. Linear Active Disturbance Rejection Control From the Practical Aspects. IEEE/ASME Transactions on Mechatronics , 2018, 23(6): 2909–2919.

Sivanandhan, A.; Thriveni, G. Optimal design of controller for automatic voltage regulator performance enhancement: a survey. Electrical Engineering , 2024, 106: 3705–3720.

dos Santos Coelho, L. Tuning of PID controller for an automatic regulator voltage system using chaotic optimization approach. Chaos, Solitons & Fractals , 2009, 39(4): 1504–1514.

Odili, J. B.; Mohmad Kahar, M. N.; Ahmad, N. Parameters-tuning of PID controller for automatic voltage regulators using the African buffalo optimization. PLOS ONE , 2017, 12(4): e0175901.

Zhou, G.; Li, J.; Tang, Z.; Luo, Q.; Zhou, Y. An improved spotted hyena optimizer for PID parameters in an AVR system. Mathematical Biosciences and Engineering , 2020, 17(4): 3767–3783.

Micev, M.; Ćalasan, M.; Radulović, M. Optimal tuning of the novel voltage regulation controller considering the real model of the automatic voltage regulation system. Heliyon , 2023, 9(8): e18707.

Yu, R.; Ho, G. K. Y.; Pong, B. M. H.; Ling, B. W.-K.; Lam, J. Computer-Aided Design and Optimization of High-Efficiency LLC Series Resonant Converter. IEEE Transactions on Power Electronics , 2012, 27(7): 3243–3256.

Zhou, B.; Zhang, M.; Luo, H.; Qiu, Y.; Chen, S.; Zang, T.; Zhou, Y.; Zhou, X. Multi-objectives optimization of parameter design for LLC converter based on data-driven surrogate model. IET Power Electronics , 2024, 17(10).