Enhanced gorilla troops optimizer powered by marine predator algorithm: global optimization and engineering design

This study presents an advanced metaheuristic approach termed the Enhanced Gorilla Troops Optimizer (EGTO), which builds upon the Marine Predators Algorithm (MPA) to enhance the search capabilities of the Gorilla Troops Optimizer (GTO). Like numerous other metaheuristic algorithms, the GTO encounter...

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Vydáno v:Scientific reports Ročník 14; číslo 1; s. 7650 - 47
Hlavní autoři: Hassan, Mohamed H., Kamel, Salah, Mohamed, Ali Wagdy
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Nature Publishing Group UK 01.04.2024
Nature Publishing Group
Nature Portfolio
Témata:
639/705/1041
639/705/1042
639/705/117
Algorithms
Benchmarking
CEC2019
Comparative analysis
Convergence
Data Compression
Design engineering
Gorilla troops optimizer
Humanities and Social Sciences
Marine predator algorithm
multidisciplinary
Optimization algorithms
Optimization techniques
Predators
Science
Science (multidisciplinary)
Swarm intelligence
CEC2019
Marine predator algorithm
Swarm intelligence
Gorilla troops optimizer
ISSN:2045-2322, 2045-2322
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Shrnutí:This study presents an advanced metaheuristic approach termed the Enhanced Gorilla Troops Optimizer (EGTO), which builds upon the Marine Predators Algorithm (MPA) to enhance the search capabilities of the Gorilla Troops Optimizer (GTO). Like numerous other metaheuristic algorithms, the GTO encounters difficulties in preserving convergence accuracy and stability, notably when tackling intricate and adaptable optimization problems, especially when compared to more advanced optimization techniques. Addressing these challenges and aiming for improved performance, this paper proposes the EGTO, integrating high and low-velocity ratios inspired by the MPA. The EGTO technique effectively balances exploration and exploitation phases, achieving impressive results by utilizing fewer parameters and operations. Evaluation on a diverse array of benchmark functions, comprising 23 established functions and ten complex ones from the CEC2019 benchmark, highlights its performance. Comparative analysis against established optimization techniques reveals EGTO's superiority, consistently outperforming its counterparts such as tuna swarm optimization, grey wolf optimizer, gradient based optimizer, artificial rabbits optimization algorithm, pelican optimization algorithm, Runge Kutta optimization algorithm (RUN), and original GTO algorithms across various test functions. Furthermore, EGTO's efficacy extends to addressing seven challenging engineering design problems, encompassing three-bar truss design, compression spring design, pressure vessel design, cantilever beam design, welded beam design, speed reducer design, and gear train design. The results showcase EGTO's robust convergence rate, its adeptness in locating local/global optima, and its supremacy over alternative methodologies explored.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-57098-8