Improved HHO algorithm based on good point set and nonlinear convergence formula

doi:10.19682/j.cnki.1005-8885.2021.1005

中国邮电高校学报(英文版) ›› 2021, Vol. 28 ›› Issue (2): 48-67.doi: 10.19682/j.cnki.1005-8885.2021.1005

• Artificial Intelligence • 上一篇下一篇

Improved HHO algorithm based on good point set and nonlinear convergence formula

Guo Hairu, Meng Xueyao, Liu Yongli, Liu Shen

School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo 454000, China

收稿日期:2020-10-23 修回日期:2021-02-26 出版日期:2021-04-30 发布日期:2021-04-30
通讯作者: Liu Yongli E-mail:yongli.buaa@gmail.com

Improved HHO algorithm based on good point set and nonlinear convergence formula

Guo Hairu, Meng Xueyao, Liu Yongli, Liu Shen

School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo 454000, China

Received:2020-10-23 Revised:2021-02-26 Online:2021-04-30 Published:2021-04-30

摘要/Abstract

摘要： Harris hawks optimization ( HHO) algorithm is an efficient method of solving function optimization problems.
However, it is still confronted with some limitations in terms of low precision, low convergence speed and stagnation
to local optimum. To this end, an improved HHO ( IHHO) algorithm based on good point set and nonlinear
convergence formula is proposed. First, a good point set is used to initialize the positions of the population
uniformly and randomly in the whole search area. Second, a nonlinear exponential convergence formula is designed
to balance exploration stage and exploitation stage of IHHO algorithm, aiming to find all the areas containing the
solutions more comprehensively and accurately. The proposed IHHO algorithm tests 17 functions and uses Wilcoxon
test to verify the effectiveness. The results indicate that IHHO algorithm not only has faster convergence speed than
other comparative algorithms, but also improves the accuracy of solution effectively and enhances its robustness
under low dimensional and high dimensional conditions.

关键词: HHO algorithm, local optimum, good point set, nonlinear formula, multi-dimension

Abstract: Harris hawks optimization ( HHO) algorithm is an efficient method of solving function optimization problems.
However, it is still confronted with some limitations in terms of low precision, low convergence speed and stagnation
to local optimum. To this end, an improved HHO ( IHHO) algorithm based on good point set and nonlinear
convergence formula is proposed. First, a good point set is used to initialize the positions of the population
uniformly and randomly in the whole search area. Second, a nonlinear exponential convergence formula is designed
to balance exploration stage and exploitation stage of IHHO algorithm, aiming to find all the areas containing the
solutions more comprehensively and accurately. The proposed IHHO algorithm tests 17 functions and uses Wilcoxon
test to verify the effectiveness. The results indicate that IHHO algorithm not only has faster convergence speed than
other comparative algorithms, but also improves the accuracy of solution effectively and enhances its robustness
under low dimensional and high dimensional conditions.

Key words: HHO algorithm, local optimum, good point set, nonlinear formula, multi-dimension

中图分类号:

TP391.1

Guo Hairu, Meng Xueyao, Liu Yongli, Liu Shen. Improved HHO algorithm based on good point set and nonlinear convergence formula [J]. The Journal of China Universities of Posts and Telecommunications, 2021, 28(2): 48-67.

参考文献

1. Fei W, Liu C, Hu S. Research on swarm intelligence optimization algorithm. Journal of China Universities of Posts and Telecommunications, 2020, 27(3): 1-20

2. Saraswathi M, Murali G B, Deepak B B V L. Optimal path planning of mobile robot using hybrid cuckoo search-bat algorithm. Procedia Computer Science, 2018, 133: 510-517

3. El-Aziz M A, Ewees A A, Hassanien A E. Whale optimization algorithm and moth-flame optimization for multilevel thresholding image segmentation. Expert Systems with Applications, 2017, 83: 242-256

4. Zhou Y H, Su K, Shao L M. A new chaotic hybrid cognitive optimization algorithm. Cognitive Systems Research, 2018, 52: 537-542

5. Sharma H, Bansal J C, Arya K V, et al. Lévy flight artificial bee colony algorithm. International Journal of Systems Science, 2016, 47(11): 2652-2670

6. Yang Q, Chen W N, Yu Z T, et al. Adaptive multimodal continuous ant colony optimization. IEEE Trans on Evolutionary Computation, 2017, 21(2): 191-205

7. Yelghi A, Köse C. A modified firefly algorithm for global minimum optimization. Applied Soft Computing, 2018, 62: 29-44

8. Sayed G I, Darwish A, Hassanien A E. A new chaotic whale optimization algorithm for features selection. Journal of Classification, 2018, 35(2): 300-344

9. Kohli M, Arora S. Chaotic grey wolf optimization algorithm for constrained optimization problems. Journal of Computational Design and Engineering, 2018, 5(4): 458-472

10. Haidari A A, Mirjalili S, Faris H, et al. Harris hawks optimization: algorithm and applications. Future Generation Computer Systems, 2019, 97: 849-872

11. Sihwail R, Omar K, Ariffin K A Z, et al. Improved Harris hawks optimization using elite opposition-based learning and novel search mechanism for feature selection. IEEE Access, 2020, 8: 121127-121145

12. Rodriguez-Esparza E, Zanella-Calzada L A, Oliva D, et al. An efficient Harris hawks-inspired image segmentation method. Expert Systems with Applications, 2020. 155: Article 113428

13. Chen H, Jiao S, Wang M, et al. Parameters identification of photovoltaic cells and modules using diversification-enriched Harris hawks optimization with chaotic drifts. Journal of Cleaner Production, 2020, 244: Article 118778

14. Yousri D, Allam D, Eteiba M B. Optimal photovoltaic array reconfiguration for alleviating the partial shading influence based on a modified Harris hawks optimizer. Energy Conversion and Management, 2020, 206: Article 112470

15. Rammurthy D, Mahesh P K. Whale Harris hawks optimization based deep learning classifier for brain tumor detection using MRI images. Journal of King Saud University--Computer and Information Sciences, 2020, DOI: 10.1016/j.jksuci.2020.08.006

16. Hussain K, Zhu W, Salleh M N M. Long-term memory Harris’ hawk optimization for high dimensional and optimal power flow problems. IEEE Access, 2019, 7: 147596-147616

17. Bao X L, Jia H M, Lang C B. A novel hybrid Harris hawks optimization for color image multilevel thresholding segmentation. IEEE Access, 2019, 7: 76529-76546

18. Jia H M, Lang C B, Oliva D, et al. Dynamic Harris hawks optimization with mutation mechanism for satellite image segmentation. Remote Sensing, 2019, 11(12): Article 1421

19. Gupta S, Deep K, Heidari A A, et al. Opposition-based learning Harris hawks optimization with advanced transition rules: principles and analysis. Expert Systems with Applications, 2020, 158: Article 113510

20. Chen H, Heidari A A, Chen H L, et al. Multi-population differential evolution-assisted Harris hawks optimization: framework and case studies. Future Generation Computer Systems, 2020, 111: 175-198

21. Yan H W, Cao Y L, Yang J X. Statistical tolerance analysis based on good point set and homogeneous transform matrix. Procedia CIRP, 2016, 43: 178-183

22. Wang C F, Liu K, Shen P P. A novel genetic algorithm for global optimization. Acta Mathematicae Applicatae Sinica: English Series, 2020, 36(2): 482-491

23. Rashedi E, Nezamabadi-Pour H, Saryazdi S. GSA: a gravitational search algorithm. Information Sciences, 2009, 179(13): 2232-2248

24. Mirjalili S, Lewis A. The whale optimization algorithm. Advances in Engineering Software, 2016, 95: 51-67

25. Mittal N, Singh U, Sohi B S. Modified grey wolf optimizer for global engineering optimization. Applied Computational Intelligence and Soft Computing, 2016, DOI: 10.1155/2016/7950348

26. Mirjalili S, Mirjalili S M, Lewis A. Grey wolf optimizer. Advances in Engineering Software, 2014, 69: 46-61

27. Derrac J, García S, Molina D, et al. A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm and Evolutionary Computation, 2011, 1(1): 3-18

Improved HHO algorithm based on good point set and nonlinear convergence formula

Improved HHO algorithm based on good point set and nonlinear convergence formula

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 1

编辑推荐

Metrics

本文评价