article · Artificial Intelligence Review
The Artificial Gorilla Troops Optimizer is a nature-inspired computational algorithm modelled on the group behaviour of gorillas. Designed to solve complex constrained and unconstrained optimisation problems, the method uses a flexible weighting mechanism without relying on derivatives or complex parameter tuning. A comprehensive evaluation of 112 published studies demonstrates its performance, straightforward structure, and high adaptability across diverse problem scenarios. The research examines the algorithm's evolution, structural enhancements designed to align search area geometry with practical problems, and the organisation of a dedicated solver tool. While assessing its convergence capabilities, the review identifies primary operational limitations and outlines prospective directions for future algorithmic adjustments and enhancements.
Optimisation algorithms help computer programmes find the most efficient solutions to complex real-world dilemmas, from resource allocation to engineering design. By evaluating how the gorilla troops method performs across over a hundred studies, this work helps practitioners understand a powerful, parameter-free tool capable of tackling difficult mathematical challenges without requiring heavy manual configuration.
The algorithm offers utility for engineers and data scientists seeking to solve complex mathematical and operational optimisation challenges without extensive parameter tuning. The review of a dedicated solver indicates an applied and tested stage of development across diverse problem scenarios, though the abstract does not cite specific commercial products, industry partners, or direct enterprise deployments.
AI-generated from the published abstract. Always read the original work before citing.
Abstract 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.
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.1007/s10462-024-10838-8
Is something wrong with this record? Report it or request removal.
Discussion
Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.
No discussion yet. Open the first thread.
New to MARATTO™? Create a free account.
