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Al-Biruni Earth Radius (BER) Metaheuristic Search Optimization Algorithm

El-Sayed M. El-kenawy1,2, Abdelaziz A. Abdelhamid3,4, Abdelhameed Ibrahim5, Seyedali Mirjalili6,7, Nima Khodadad8, Mona A. Al duailij9, Amel Ali Alhussan9,*, Doaa Sami Khafaga9
1 Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura, 35111, Egypt
2 Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura, 35712, Egypt
3 Department of Computer Science, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, 11566, Egypt
4 Department of Computer Science, College of Computing and Information Technology, Shaqra University, 11961, Saudi Arabia
5 Computer Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Mansoura, 35516, Egypt
6 Centre for Artificial Intelligence Research and Optimization, Torrens University Australia, Fortitude Valley, QLD 4006, Australia
7 Yonsei Frontier Lab, Yonsei University, Seoul, 03722, Korea
8 Department of Civil and Environmental Engineering, Florida International University, Miami, USA
9 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
* Corresponding Author: Amel Ali Alhussan. Email:

Computer Systems Science and Engineering 2023, 45(2), 1917-1934. https://doi.org/10.32604/csse.2023.032497

Received 20 May 2022; Accepted 21 June 2022; Issue published 03 November 2022

Abstract

Metaheuristic optimization algorithms present an effective method for solving several optimization problems from various types of applications and fields. Several metaheuristics and evolutionary optimization algorithms have been emerged recently in the literature and gained widespread attention, such as particle swarm optimization (PSO), whale optimization algorithm (WOA), grey wolf optimization algorithm (GWO), genetic algorithm (GA), and gravitational search algorithm (GSA). According to the literature, no one metaheuristic optimization algorithm can handle all present optimization problems. Hence novel optimization methodologies are still needed. The Al-Biruni earth radius (BER) search optimization algorithm is proposed in this paper. The proposed algorithm was motivated by the behavior of swarm members in achieving their global goals. The search space around local solutions to be explored is determined by Al-Biruni earth radius calculation method. A comparative analysis with existing state-of-the-art optimization algorithms corroborated the findings of BER’s validation and testing against seven mathematical optimization problems. The results show that BER can both explore and avoid local optima. BER has also been tested on an engineering design optimization problem. The results reveal that, in terms of performance and capability, BER outperforms the performance of state-of-the-art metaheuristic optimization algorithms.

Keywords

Metaheuristics; evolutionary optimization; exploration; exploitation; mutation; Al-biruni earth radius

Cite This Article

E. M. El-kenawy, A. A. Abdelhamid, A. Ibrahim, S. Mirjalili, N. Khodadad et al., "Al-biruni earth radius (ber) metaheuristic search optimization algorithm," Computer Systems Science and Engineering, vol. 45, no.2, pp. 1917–1934, 2023.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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