Volume 34, Issue 1 (IJIEPR 2023)                   IJIEPR 2023, 34(1): 1-9 | Back to browse issues page


XML Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Gomaa I, Zaher H, Ragaa Saeid N, Sayed H. A Novel Enhanced Gorilla Troops Optimizer Algorithm for Global Optimization Problems. IJIEPR 2023; 34 (1) :1-9
URL: http://ijiepr.iust.ac.ir/article-1-1620-en.html
1- Faculty of graduate studies for statistical research, Cairo University , islamhgomaa@gmail.com
2- Department of Statistics, Faculty of graduate studies for statistical research, Cairo University, Cairo, Egypt.
3- Department of Operations Research, Faculty of graduate studies for statistical research, Cairo University, Cairo, Egypt
Abstract:   (1376 Views)
Researchers in many fields, such as operations research, computer science, AI engineering, and mathematical engineering, extra, are increasingly adopting nature-inspired metaheuristic algorithms because of their simplicity and flexibility. Natural metaheuristic algorithms are based on two essential terms: exploration (diversification) and exploitation (intensification). The success and limitations of these algorithms are reliant on the tuning and control of their parameters. When it comes to tackling real optimization problems, the Gorilla Troop Optimizer (GTO) is an extremely effective algorithm that is inspired by the social behavior of gorilla troops. Three operators of the original GTO algorithm are committed to exploration, and the other two operators are dedicated to exploitation. Even though the superiority of GTO algorithm to several metaheuristic algorithms, it needs to improve the balance between the exploration process and the exploitation process to ensure an accurate estimate of the global optimum. For this reason, a Novel Enhanced version of GTO (NEGTO), which focuses on the correct balance of exploration and exploitation, has been proposed. This paper suggests a novel modification on the original GTO to enhance the exploration process and exploitation process respectively, through introducing a dynamic controlling parameter and improving some equations in the original algorithm based on the new controlling parameter. A computational experiment is conducted on a set of well-known benchmark test functions used to show that NEGTO outperforms the standard GTO and other well-known algorithms in terms of efficiency, effectiveness, and stability. The proposed NEGTO for solving global optimization problems outperforms the original GTO in most unimodal benchmark test functions and most multimodal benchmark test functions, a wider search space and more intensification search of the global optimal solution are the main advantages of the proposed NEGTO.
Full-Text [PDF 865 kb]   (854 Downloads)    
Type of Study: Research | Subject: Optimization Techniques
Received: 2022/10/15 | Accepted: 2023/01/23 | Published: 2023/03/10

Add your comments about this article : Your username or Email:
CAPTCHA

Send email to the article author


Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.