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Full Length Article
Volume 3 , Issue 1, PP: 01-28 , 2021


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   :  10.5281/zenodo.4621285

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

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