M. Jahir Bin Alam, M.a. Ansery, R.k Chowdhuary, J. Uddin Ahmed, S. Islam , S. Rahman ,
Volume 19, Issue 3 (International Journal of Engineering 2008)
Abstract
Abstract: Sylhet is the northeastern region of Bangladesh and probability of earthquake in Sylhet is higher than other areas of this zone. Among 27 wards, Ward no. 14 is one of the important largest Wards in Sylhet city and a densely populated one. It was clear from the survey works, 42.8% buildings are belongs to Building with RCC frame 54.03% buildings are Masonry buildings. Another interesting finding is 325 houses fall in the category of Houses with resident 1-10. The occurrence of an earthquake of PGA value 0.9g on ward no. 14 causes massive loss of lives and damage to buildings. Depending on the time of the day 147 to 603 people may be killed due to structural collapse and the buildings of worth approximately TK.32.00 core may be damaged.
Islam Gomaa, Hegazy Zaher, Naglaa Ragaa Saeid, Heba Sayed ,
Volume 34, Issue 1 (IJIEPR 2023)
Abstract
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.
