970 565
Full Length Article
Fusion: Practice and Applications
Volume 3 , Issue 1, PP: 01-28 , 2021 | Cite this article as | XML | Html |PDF


An Improved Equilibrium Optimizer Algorithm for Tackling Global Optimization Problems

Authors Names :   Samia Mandour   1     Ibrahim el-henawy   2     Kareem Ahmed   3  

1  Affiliation :  Zagazig University, Zagazig, Egypt

    Email :  eng.samia.mandour@gmail.com

2  Affiliation :  Zagazig University, Zagazig, Egypt

    Email :  ielhenawy@zu.edu.eg

3  Affiliation :  Computer Science department, Beni-Suef University, Egypt

    Email :  Kareem_ahmed@hotmail.co.uk

Doi   :   https://doi.org/10.54216/FPA.030101

Received October 10, 2020 Revised February 22, 2021 Accepted March 11, 2021

Abstract :

This paper introduces a new, metaheuristic optimization algorithm, named an Improved Metaheuristic Equilibrium Optimizer (IMEO). The algorithm Equilibrium Optimizer (EO), is inspired by its method of estimating both equilibrium and dynamics, based on mass balance models. Studying the EO closely, we find that EO does not have the potential to get closer to the optimal global solution when it solves certain problems. To improve EO efficiency, this paper suggests using an improvement, called an elite opposition learning-based, that increases the speed and accuracy of EO convergence, and helps the algorithm to get a better solution. Falling into local optima is a big problem, EO suffers from the fact that when we look deeply at the standard EO mathematical formula, we found that the algorithm is trying to get out of the local optima, but sometimes it can't, so we're introducing a new mathematical formula based on using cosine trigonometric function. To validate the proposed algorithm efficiency, The IMEO algorithm is evaluated on 23 benchmarks and compared with other common naturalistic heuristic algorithms. The statistical analysis shows that the results of IMEO achieve better performance compared to the standard EO and several well-known algorithms on several benchmark issues.

Keywords :

Meta-heuristic algorithms , Equilibrium Optimizer algorithm , Elite opposition-based learning strategy , Benchmark problems

References :

1.         Abdel-Basset, M., et al., A modified flower pollination algorithm for the multidimensional knapsack problem: human-centric decision making. Soft Computing, 2018. 22(13): p. 4221-4239.

2.         Törn, A. and A. Zilinskas, Global optimization. 1989.

3.         Mirjalili, S., S.M. Mirjalili, and A. Lewis, Grey wolf optimizer. Advances in engineering software, 2014. 69: p. 46-61.

4.         Yan, F., X. Xu, and J. Xu, Grey wolf optimizer with a novel weighted distance for global optimization. IEEE Access, 2020. 8: p. 120173-120197.

5.         Luo, J. and Z. Liu, Novel grey wolf optimization based on modified differential evolution for numerical function optimization. Applied Intelligence, 2020. 50(2): p. 468-486.

6.         Mirjalili, S., et al., Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems. Advances in Engineering Software, 2017. 114: p. 163-191.

7.         Nautiyal, B., et al., Improved Salp Swarm Algorithm with mutation schemes for solving global optimization and engineering problems. Engineering with Computers, 2021: p. 1-23.

8.         Salgotra, R., et al., Self-adaptive salp swarm algorithm for engineering optimization problems. Applied Mathematical Modelling, 2021. 89: p. 188-207.

9.         Mirjalili, S. and A. Lewis, The whale optimization algorithm. Advances in engineering software, 2016. 95: p. 51-67.

10.       Chakraborty, S., et al., A novel enhanced whale optimization algorithm for global optimization. Computers & Industrial Engineering, 2021. 153: p. 107086.

11.       Fan, Q., et al., ESSAWOA: Enhanced Whale Optimization Algorithm integrated with Salp Swarm Algorithm for global optimization. Engineering with Computers, 2020: p. 1-18.

12.       Mirjalili, S., SCA: a sine cosine algorithm for solving optimization problems. Knowledge-based systems, 2016. 96: p. 120-133.

13.       Chen, H., M. Wang, and X. Zhao, A multi-strategy enhanced sine cosine algorithm for global optimization and constrained practical engineering problems. Applied Mathematics and Computation, 2020. 369: p. 124872.

14.       Gupta, S., K. Deep, and A.P. Engelbrecht, A memory guided sine cosine algorithm for global optimization. Engineering Applications of Artificial Intelligence, 2020. 93: p. 103718.

15.       Gupta, S., et al., A modified sine cosine algorithm with novel transition parameter and mutation operator for global optimization. Expert Systems with Applications, 2020. 154: p. 113395.

16.       Mirjalili, S. and S.Z.M. Hashim. A new hybrid PSOGSA algorithm for function optimization. in 2010 international conference on computer and information application. 2010. IEEE.

17.       Dhiman, G., et al., EMoSOA: a new evolutionary multi-objective seagull optimization algorithm for global optimization. International Journal of Machine Learning and Cybernetics, 2021. 12(2): p. 571-596.

18.       Dhiman, G., et al., A novel algorithm for global optimization: Rat swarm optimizer. Journal of Ambient Intelligence and Humanized Computing, 2020: p. 1-26.

19.       Çelik, E., A powerful variant of symbiotic organisms search algorithm for global optimization. Engineering Applications of Artificial Intelligence, 2020. 87: p. 103294.

20.       Hu, Z., et al., Grey prediction evolution algorithm for global optimization. Applied Mathematical Modelling, 2020. 79: p. 145-160.

21.       Rodrigues, D., et al., Adaptive improved flower pollination algorithm for global optimization, in Nature-Inspired Computation in Data Mining and Machine Learning. 2020, Springer. p. 1-21.

22.       Hu, K., et al., A modified butterfly optimization algorithm: An adaptive algorithm for global optimization and the support vector machine. Expert Systems, 2020: p. e12642.

23.       Kaur, S., et al., Tunicate swarm algorithm: a new bio-inspired based metaheuristic paradigm for global optimization. Engineering Applications of Artificial Intelligence, 2020. 90: p. 103541.

24.       Faramarzi, A., et al., Equilibrium optimizer: A novel optimization algorithm. Knowledge-Based Systems, 2020. 191: p. 105190.

25.       Gao, Y., Y. Zhou, and Q. Luo, An efficient binary equilibrium optimizer algorithm for feature selection. IEEE Access, 2020. 8: p. 140936-140963.

26.       Menesy, A.S., H.M. Sultan, and S. Kamel. Extracting model parameters of proton exchange membrane fuel cell using equilibrium optimizer algorithm. in 2020 International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE). 2020. IEEE.

27.       Abdel-Basset, M., et al., Solar photovoltaic parameter estimation using an improved equilibrium optimizer. Solar Energy, 2020. 209: p. 694-708.

28.       Rabehi, A., et al., Optimal estimation of Schottky diode parameters using a novel optimization algorithm: Equilibrium optimizer. Superlattices and Microstructures, 2020. 146: p. 106665.

29.       Agnihotri, S., A. Atre, and H. Verma. Equilibrium optimizer for solving economic dispatch problem. in 2020 IEEE 9th Power India International Conference (PIICON). 2020. IEEE.

30.       Micev, M., M. ─ćalasan, and D. Oliva, Design and robustness analysis of an Automatic Voltage Regulator system controller by using Equilibrium Optimizer algorithm. Computers & Electrical Engineering, 2021. 89: p. 106930.

31.       Wunnava, A., et al., A novel interdependence based multilevel thresholding technique using adaptive equilibrium optimizer. Engineering Applications of Artificial Intelligence, 2020. 94: p. 103836.

32.       Zhou, Y., R. Wang, and Q. Luo, Elite opposition-based flower pollination algorithm. Neurocomputing, 2016. 188: p. 294-310.

33.       Mohamed, A.-A.A., et al., Optimal power flow using moth swarm algorithm. Electric Power Systems Research, 2017. 142: p. 190-206.

Cite this Article as :
Samia Mandour , Ibrahim el-henawy , Kareem Ahmed, An Improved Equilibrium Optimizer Algorithm for Tackling Global Optimization Problems, Fusion: Practice and Applications, Vol. 3 , No. 1 , (2021) : 01-28 (Doi   :  https://doi.org/10.54216/FPA.030101)