International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 07 | July 2023
p-ISSN: 2395-0072
www.irjet.net
Improved F-parameter Mountain Gazelle Optimizer (IFMGO): A Comparative Analysis on Engineering Design Problems Abdul-Fatawu Seini Yussif1, Toufic Seini 2 1 Department of Electrical and Electronic Engineering, Kwame Nkrumah University of Science and Technology,
Kumasi, Ghana
2Department of Physical Sciences, University for Development Studies, Ghana
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Abstract - This paper presents a comparative study of three
to the presence of conflicting and competing objectives, coupled with the high dimensionality and non-linearity of the design space.
metaheuristic algorithms: the Improved F-parameter Mountain Gazelle Optimizer (IFMGO), the Mountain Gazelle Optimizer (MGO), and the Particle Swarm Optimization (PSO) algorithm, applied to a selection of challenging engineering design problems. IFMGO, an advanced version of MGO, demonstrates enhanced exploration and exploitation capabilities owing to its inspiration from the social behavior of mountain gazelles. The algorithms were implemented in the MATLAB environment and evaluated on diverse engineering design problems, including the Pressure Vessel Design Problem (PVDP), the Spring Design Problem (SDP), the Three-bar Truss Design Problem (TTDP), the Cantilever Beam Design Problem (CBDP), and the Welded Beam Design Problem (WBDP). The primary objective is to investigate if IFMGO’s improvements over MGO would lead to superior performance in solving engineering optimization problems. Our experimental results demonstrate that IFMGO indeed outperforms MGO across all the engineering design problems considered. Furthermore, IFMGO showcases competitive performance when compared to the well-established PSO algorithm, a testament to its efficacy as a tool for handling intricate engineering design challenges.
The IFMGO algorithm demonstrates superior exploration and exploitation capabilities in comparison to MGO, which is based on the social intelligence of mountain gazelles in the wildlife [4][8]. The enhancements introduced in the IFMGO aimed to address certain limitations present in the MGO, making it more adept at tackling complex, multi-dimensional engineering optimization problems. To ascertain the performance of IFMGO in comparison to MGO and PSO, these algorithms have been implemented and tested using MATLAB software, a widely-adopted and robust computational environment. The choice of engineering design problems for evaluation includes the Pressure Vessel Design Problem, the String Design Problem, the Three-bar Truss Design Problem, the Cantilever Beam Design Problem, and the Welded Beam Design Problem [9][10][11]. These problems are well-known benchmarks in the field of engineering optimization, covering a diverse range of complexities and dimensions. Initial results from our experimentation demonstrated that the IFMGO algorithm exhibits remarkable superiority over MGO in all the engineering design problems considered. Moreover, IFMGO demonstrates competitive performance compared to the well-established PSO algorithm. The objective of this paper is to shed light on the strengths and weaknesses of these algorithms, providing valuable insights for researchers and practitioners seeking efficient optimization strategies for engineering design tasks.
Key Words: Algorithm, optimization, mountain gazelle, engineering design problems, metaheuristic algorithm.
1.INTRODUCTION This In the pursuit of optimizing complex engineering design problems, metaheuristic algorithms have emerged as promising tools that can efficiently handle non-linear, multiobjective optimization challenges [1][2]. Among these algorithms, the Improved F-parameter Mountain Gazelle Optimizer (IFMGO) [3] presents a significant advancement over its predecessor, the Mountain Gazelle Optimizer (MGO) [4]. This paper aims to investigate and compare the performance of IFMGO, MGO, and Particle Swarm Optimization (PSO) on a set of diverse engineering design problems [3][4][5].
The subsequent sections of this paper will delve into the detailed methodology employed, the mathematical formulation of the IFMGO algorithm, the experimental setup, and comprehensive analyses of the obtained results. Finally, the implication of the findings in the context of engineering design optimization would be discussed and concluded with recommendations for future research avenues in the realm of metaheuristic algorithms.
Engineering design optimization plays a pivotal role in various industries, including aerospace, mechanical, civil, and structural engineering, among others [6][7]. The main objective is to find the optimal design parameters that satisfy multiple objectives while considering a range of constraints. However, this task often presents a formidable challenge due
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This study, therefore, seeks to contribute to the growing body of knowledge in the field of engineering optimization and further establish the significance of the IFMGO algorithm as a powerful tool for tackling complex engineering design problems.
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