Model Order Reduction of Continuous Time Systems using Artificial Gorilla Troops Algorithm and Routh Approximation

Padhy, Aditya Prasad; Jena, Kasinath; Pratyasha, Prateek

doi:10.1109/inc457730.2023.10263024

2023 IEEE International Conference on Contemporary Computing and Communications (InC4) 2023

DOI: 10.1109/inc457730.2023.10263024

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Model Order Reduction of Continuous Time Systems using Artificial Gorilla Troops Algorithm and Routh Approximation

Aditya Prasad Padhy,

Kasinath Jena,

Prateek Pratyasha

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An in-depth survey of the artificial gorilla troops optimizer: outcomes, variations, and applications

Hussien,

Bouaouda,

Alzaqebah

et al. 2024

Artif Intell Rev

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A recently developed algorithm inspired by natural processes, known as the Artificial Gorilla Troops Optimizer (GTO), boasts a straightforward structure, unique stabilizing features, and notably high effectiveness. Its primary objective is to efficiently find solutions for a wide array of challenges, whether they involve constraints or not. The GTO takes its inspiration from the behavior of Gorilla Troops in the natural world. To emulate the impact of gorillas at each stage of the search process, the GTO employs a flexible weighting mechanism rooted in its concept. Its exceptional qualities, including its independence from derivatives, lack of parameters, user-friendliness, adaptability, and simplicity, have resulted in its rapid adoption for addressing various optimization challenges. This review is dedicated to the examination and discussion of the foundational research that forms the basis of the GTO. It delves into the evolution of this algorithm, drawing insights from 112 research studies that highlight its effectiveness. Additionally, it explores proposed enhancements to the GTO’s behavior, with a specific focus on aligning the geometry of the search area with real-world optimization problems. The review also introduces the GTO solver, providing details about its identification and organization, and demonstrates its application in various optimization scenarios. Furthermore, it provides a critical assessment of the convergence behavior while addressing the primary limitation of the GTO. In conclusion, this review summarizes the key findings of the study and suggests potential avenues for future advancements and adaptations related to the GTO.

show abstract

An in-depth survey of the artificial gorilla troops optimizer: outcomes, variations, and applications

Hussien,

Bouaouda,

Alzaqebah

et al. 2024

Artif Intell Rev

View full text Add to dashboard Cite

show abstract

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Model Order Reduction of Continuous Time Systems using Artificial Gorilla Troops Algorithm and Routh Approximation

Cited by 1 publication

References 16 publications

An in-depth survey of the artificial gorilla troops optimizer: outcomes, variations, and applications

An in-depth survey of the artificial gorilla troops optimizer: outcomes, variations, and applications

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