The Mountain Gazelle Optimizer for truss structures optimization

Nima Khodadadi; El-Sayed M. El-Kenawy; Francisco De Caso; Amal H. Alharbi; Doaa Sami Khafaga; Antonio Nanni; Nima Khodadadi; El-Sayed M. El-Kenawy; Francisco De Caso; Amal H. Alharbi; Doaa Sami Khafaga; Antonio Nanni

doi:10.3934/aci.2023007

Applied Computing and Intelligence

2023, Volume 3, Issue 2: 116-144. doi: 10.3934/aci.2023007

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Research article

The Mountain Gazelle Optimizer for truss structures optimization

1.
Department of Civil and Architectural Engineering, University of Miami, Coral Gables, FL, USA; nima.khodadadi@miami.edu, fdecaso@miami.edu, nanni@miami.edu
2.
Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt; skenawy@ieee.org
3.
Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia; ahalharbi@pnu.edu.sa, dskhafga@pnu.edu.sa

Academic Editor: Jidong Yang

Received: 23 May 2023 Revised: 21 September 2023 Accepted: 26 September 2023 Published: 07 October 2023

Computational tools have been used in structural engineering design for numerous objectives, typically focusing on optimizing a design process. We first provide a detailed literature review for optimizing truss structures with metaheuristic algorithms. Then, we evaluate an effective solution for designing truss structures used in structural engineering through a method called the mountain gazelle optimizer, which is a nature-inspired meta-heuristic algorithm derived from the social behavior of wild mountain gazelles. We use benchmark problems for truss optimization and a penalty method for handling constraints. The performance of the proposed optimization algorithm will be evaluated by solving complex and challenging problems, which are common in structural engineering design. The problems include a high number of locally optimal solutions and a non-convex search space function, as these are considered suitable to evaluate the capabilities of optimization algorithms. This work is the first of its kind, as it examines the performance of the mountain gazelle optimizer applied to the structural engineering design field while assessing its ability to handle such design problems effectively. The results are compared to other optimization algorithms, showing that the mountain gazelle optimizer can provide optimal and efficient design solutions with the lowest possible weight.
- mountain gazelle optimizer (MGO) algorithms,
- optimal design,
- truss structures
Citation: Nima Khodadadi, El-Sayed M. El-Kenawy, Francisco De Caso, Amal H. Alharbi, Doaa Sami Khafaga, Antonio Nanni. The Mountain Gazelle Optimizer for truss structures optimization[J]. Applied Computing and Intelligence, 2023, 3(2): 116-144. doi: 10.3934/aci.2023007

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Abstract

Computational tools have been used in structural engineering design for numerous objectives, typically focusing on optimizing a design process. We first provide a detailed literature review for optimizing truss structures with metaheuristic algorithms. Then, we evaluate an effective solution for designing truss structures used in structural engineering through a method called the mountain gazelle optimizer, which is a nature-inspired meta-heuristic algorithm derived from the social behavior of wild mountain gazelles. We use benchmark problems for truss optimization and a penalty method for handling constraints. The performance of the proposed optimization algorithm will be evaluated by solving complex and challenging problems, which are common in structural engineering design. The problems include a high number of locally optimal solutions and a non-convex search space function, as these are considered suitable to evaluate the capabilities of optimization algorithms. This work is the first of its kind, as it examines the performance of the mountain gazelle optimizer applied to the structural engineering design field while assessing its ability to handle such design problems effectively. The results are compared to other optimization algorithms, showing that the mountain gazelle optimizer can provide optimal and efficient design solutions with the lowest possible weight.

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