Performance Enhancement of Elephant Herding Optimization Algorithm Using Modified Update Operators

Main Article Content

Abdul-Fatawu Seini Yussif
Elvis Twumasi
Emmanuel Asuming Frimpong


Optimization, swarm intelligence, nature-inspired algorithm, elephant herding optimization, matriarch


This research paper presents a modified version of the Elephant Herding Optimization (EHO) algorithm, referred to as the Modified Elephant Herding Optimization (MEHO) algorithm, to enhance its global performance. The focus of this study lies in improving the balance between exploration and exploitation within the algorithm through the modification of two key operators: the matriarch updating operator and the separation updating operator. By reframing the equations governing these operators, the proposed modifications aim to enhance the algorithm’s ability to discover optimal global solutions. The MEHO algorithm is implemented in the MATLAB environment, utilizing MATLAB R2019a. To assess its efficacy, the algorithm is subjected to rigorous testing on various standard benchmark functions. Comparative evaluations are conducted against the original EHO algorithm, as well as other established optimization algorithms, namely the Improved Elephant Herding Optimization (IEHO) algorithm, Particle Swarm Optimization (PSO) algorithm, and Biogeography-Based Optimization (BBO) algorithm. The evaluation metrics primarily focus on the algorithms’ capacity to produce the best global solution for the tested functions. The proposed MEHO algorithm outperformed the other algorithms on 75% of the tested functions, and 62.5% under two specific test scenarios. The findings highlight the effectiveness of the proposed modification in enhancing the global performance of the Elephant Herding Optimization algorithm. Overall, this work contributes to the field of optimization algorithms by presenting a refined version of the EHO algorithm that exhibits improved global search capabilities.


T. Dokeroglu, E. Sevinc, T. Kucukyilmaz, and A. Cosar, “A survey on new generation metaheuristic algorithms,” Comput. Ind. Eng., vol. 137, no. August, p. 106040, 2019, doi: 10.1016/j.cie.2019.106040.

Z. Meng, G. Li, X. Wang, S. M. Sait, and A. R. Yıldız, “A Comparative Study of Metaheuristic Algorithms for Reliability-Based Design Optimization Problems,” Arch. Comput. Methods Eng., vol. 28, no. 3, pp. 1853–1869, 2021, doi: 10.1007/s11831-020-09443-z.

X. S. Yang, “Swarm intelligence based algorithms: A critical analysis,” Evol. Intell., vol. 7, no. 1, pp. 17–28, 2014, doi: 10.1007/s12065-013-0102-2.

R. Jangra and R. Kait, “Analysis and comparison among Ant System; Ant Colony System and Max-Min Ant System with different parameters setting,” 3rd IEEE Int. Conf. , pp. 1–4, 2017, doi: 10.1109/CIACT.2017.7977376.

R. M. Golubovićć, D. I. Olćan, and B. M. Kolundžija, “Particle swarm optimization algorithm and its modifications applied to EM problems,” 8th Int. Conf. Telecommun. Mod. Satell. Cable Broadcast. Serv. TELSIKS 2007, Proc. Pap., pp. 427–430, 2007, doi: 10.1109/TELSKS.2007.4376029.

W. Gao, S. Liu, and L. Huang, “A global best artificial bee colony algorithm for global optimization,” J. Comput. Appl. Math., vol. 236, no. 11, pp. 2741–2753, 2012, doi: 10.1016/

T. Liu, F. W. Cao, and Y. Zhou, “Optimization design algorithm based on artificial immune system for mechanical systems,” 2nd Int. Symp. Electron. Commer. Secur. ISECS 2009, vol. 1, no. 1, pp. 612–615, 2009, doi: 10.1109/ISECS.2009.214.

S. Mirjalili, S. M. Mirjalili, and A. Lewis, “Grey Wolf Optimizer,” Adv. Eng. Softw., vol. 69, pp. 46–61, 2014, doi: 10.1016/j.advengsoft.2013.12.007.

G. G. Wang, S. Deb, and L. D. S. Coelho, “Elephant Herding Optimization,” Proc. - 2015 3rd Int. Symp. Comput. Bus. Intell. ISCBI 2015, pp. 1–5, 2016, doi: 10.1109/ISCBI.2015.8.

E. Tuba and Z. Stanimirovic, “Elephant herding optimization algorithm for support vector machine parameters tuning,” Proc. 9th Int. Conf. Electron. Comput. Artif. Intell. ECAI 2017, vol. 2017-Janua, pp. 1–4, 2017, doi: 10.1109/ECAI.2017.8166464.

M. F. El-Naggar, M. I. Mosaad, H. M. Hasanien, T. A. AbdulFattah, and A. F. Bendary, “Elephant herding algorithm-based optimal PI controller for LVRT enhancement of wind energy conversion systems,” Ain Shams Eng. J., vol. 12, no. 1, pp. 599–608, 2021, doi: 10.1016/j.asej.2020.07.013.

H. Moayedi, M. A. Mu’azu, and L. K. Foong, “Novel swarm-based approach for predicting the cooling load of residential buildings based on social behavior of elephant herds,” Energy Build., vol. 206, p. 11, 2020, doi: 10.1016/j.enbuild.2019.109579.

A. A. K. Ismaeel, I. A. Elshaarawy, E. H. Houssein, F. H. Ismail, and A. E. Hassanien, “Enhanced Elephant Herding Optimization for Global Optimization,” IEEE Access, vol. 7, pp. 34738–34752, 2019, doi: 10.1109/ACCESS.2019.2904679.

E. Tuba, R. Capor-Hrosik, A. Alihodzic, R. Jovanovic, and M. Tuba, “Chaotic elephant herding optimization algorithm,” SAMI 2018 - IEEE 16th World Symp. Appl. Mach. Intell. Informatics Dedic. to Mem. Pioneer Robot. Antal K. Bejczy, Proc., vol. 2018-Febru, no. February, pp. 213–216, 2018, doi: 10.1109/SAMI.2018.8324842.

H. Xu et al., “Application of elephant herd optimization algorithm based on levy flight strategy in intrusion detection,” Proc. 2018 IEEE 4th Int. Symp. Wirel. Syst. within Int. Conf. Intell. Data Acquis. Adv. Comput. Syst. IDAACS-SWS 2018, pp. 16–20, 2018, doi: 10.1109/IDAACS-SWS.2018.8525848.

S. S. Chippagiri, S. Pemmada, and N. R. Patne, “Distribution Network Reconfiguration and Distributed Generation Injection Using Improved Elephant Herding Optimization,” pp. 1–6, 2020, doi: 10.1109/stpec49749.2020.9297805.

M. A. Elhosseini, R. A. El Sehiemy, Y. I. Rashwan, and X. Z. Gao, “On the performance improvement of elephant herding optimization algorithm,” Knowledge-Based Syst., vol. 166, pp. 58–70, 2019, doi: 10.1016/j.knosys.2018.12.012.

H. Muthusamy, S. Ravindran, S. Yaacob, and K. Polat, “An improved elephant herding optimization using sine–cosine mechanism and opposition based learning for global optimization problems,” Expert Syst. Appl., vol. 172, no. October 2020, p. 114607, 2021, doi: 10.1016/j.eswa.2021.114607.

N. S. Guptha, V. Balamurugan, G. Megharaj, K. N. A. Sattar, and J. D. Rose, “Cross lingual handwritten character recognition using long short term memory network with aid of elephant herding optimization algorithm,” Pattern Recognit. Lett., vol. 159, pp. 16–22, 2022, doi: 10.1016/j.patrec.2022.04.038.

M. A. S. Ali et al., “Classification of Glaucoma Based on Elephant-Herding Optimization Algorithm and Deep Belief Network,” Electron., vol. 11, no. 11, 2022, doi: 10.3390/electronics11111763.

T. Bezdan, S. Milosevic, K. Venkatachalam, M. Zivkovic, N. Bacanin, and I. Strumberger, “Optimizing Convolutional Neural Network by Hybridized Elephant Herding Optimization Algorithm for Magnetic Resonance Image Classification of Glioma Brain Tumor Grade,” 2021 Zooming Innov. Consum. Technol. Conf. ZINC 2021, pp. 171–176, 2021, doi: 10.1109/ZINC52049.2021.9499297.

H. Hakli, “BinEHO: a new binary variant based on elephant herding optimization algorithm,” Neural Comput. Appl., vol. 32, no. 22, pp. 16971–16991, 2020, doi: 10.1007/s00521-020-04917-4.

Y. Duan, C. Liu, S. Li, X. Guo, and C. Yang, “Gradient-based elephant herding optimization for cluster analysis,” Appl. Intell., vol. 52, no. 10, pp. 11606–11637, 2022, doi: 10.1007/s10489-021-03020-y.

J. Li, H. Lei, and A. H. Alavi, “10.3390@Math8091415.Pdf,” 2020.

Most read articles by the same author(s)