Article Details

Abstract

To address the unstable search and premature convergence of existing swarm intelligence algorithms in continuous optimization, as well as the insufficient robustness of single-elite guidance in complex multimodal environments, this paper proposes a Dual-Elite-Guided Swarm Intelligence Optimization (DEGSIO) algorithm. The algorithm simultaneously maintains the historical best and second-best individuals, designs an individual update rule based on their collaborative guidance, and introduces a linearly decreasing random control parameter to balance global exploration and local exploitation. The algorithm is compared with HHO, PSO, GWO, WOA, and DE on eight benchmark functions, and the Friedman test is employed. The experimental results show that DEGSIO effectively avoids premature convergence on multiple functions, is competitive in terms of accuracy, convergence speed, and stability, and achieves overall performance statistically comparable to that of HHO, which verifies the effectiveness of the dual-elite guidance strategy.

Keywords

swarm intelligence optimization; dual-elite strategy; individual update rule; continuous optimization

References

1. [1] Mohammadzadeh H，Gharehchopogh F S. A multi-agent system based for solving high-dimensional optimization problems: A case study on email spam detection [J/OL].International Journal of Communication Systems，2021，34（3）：e4670.https://doi.org/10.1002/dac.4670.
2. [2] Liu D W，Zhang W M，Tang Y，et al. Evolving Support Vector Regression Based on Improved Grey Wolf Optimization for Predicting Settlement During Construction of High-filled Roadbed [J/OL].Transportation Geotechnics，2024，45：101233.https://doi.org/10.1016/j.trgeo.2024.101233.
3. [3] Abualigah L，Diabat A，Altalhi M，et al. Improved Gradual Change-Based Harris Hawks Optimization for Real-world Engineering Design Problems [J].Engineering with Computers，2023，39（3）：1843-1883.
4. [4] Rashed N A，Ali Y H，Rashid T A. Advancements in Optimization: Critical Analysis of Evolutionary, Swarm, and Behavior-Based Algorithms [J/OL].Algorithms，2024，17（9）：416.https://doi.org/10.3390/a17090416.
5. [5] Ma S S，Liu H M，Zhan H X，et al. Efficient Maximum Iterations for Swarm Intelligence Algorithms: A Comparative Study [J].Artificial Intelligence Review，2025，58（3）：87-87.
6. [6] Sharma A，Sharma A，Dasgotra A，et al. Opposition-Based Tunicate Swarm Algorithm for Parameter Optimization of Solar Cells [J].IEEE Access，2021，9：125590-125602.
7. [7] Abualigah L. Particle Swarm Optimization: Advances, Applications, and Experimental Insights [J].Computers, Materials & Continua，2025，82（2）：1539-1592.
8. [8] Nasir M，Sadollah A，Mirjalili S，et al. A Comprehensive Review on Applications of Grey Wolf Optimizer in Energy Systems [J].Archives of Computational Methods in Engineering，2024，32（4）：1-41.
9. [9] Wang T L，Gu S W，Liu R J，et al. Cuckoo Catfish Optimizer: A new meta-heuristic Optimization Algorithm [J/OL].Artificial Intelligence Review，2025，58（10）：326.https://link.springer.com/article/10.1007/s10462-025-11291-x.
10. [10] Heidari A A，Mirjalili S，Faris H，et al. Harris Hawks Optimization: Algorithm and Applications [J].Future Generation Computer Systems，2019，97：849-872.
11. [11] Kennedy J，Eberhart R. Particle Swarm Optimization [C]//Proceedings of ICNN'95-international conference on neural networks，1995，4：1942-1948.
12. [12] Mirjalili S，Mirjalili S M，Lewis A. Grey Wolf Optimizer [J].Advances in Engineering Software，2014，69：46-61.
13. [13] Mirjalili S，Lewis A. The Whale Optimization Algorithm [J].Advances in Engineering software，2016，95：51-67.
14. [14] Storn R，Price K. Differential Evolution-A Simple and Efficient Heuristic for Global Optimization Over Continuous Spaces [J].Journal of Global Optimization，1997，11（4）：341-359.

Funding

National Natural Science Foundation of China (82260849); Development Plan for University level Science and Technology Innovation Team of Jiangxi University of Traditional Chinese Medicine (CXTD22015)

About the authors

Zheng Shuifei (1998-), male, Han ethnicity, from Shangrao, Jiangxi, currently pursuing a master's degree in data mining and traditional Chinese medicine informatics; Corresponding author: Nie Bin (1972-), male, Han ethnicity, from Xiajiang, Jiangxi, professor, CCF member, PhD, research direction: data mining, traditional Chinese medicine informatics; Zhang Zhiyao (2001-), female, Han ethnicity, from Nanchang, Jiangxi Province, is currently pursuing a master's degree. Her research interests include data mining and traditional Chinese medicine informatics; Li Xiang (2001-), male, Han ethnicity, from Tongling, Anhui Province, is currently pursuing a master's degree in data mining and traditional Chinese medicine informatics.