review · Systems Science & Control Engineering
The Bald Eagle Search algorithm is a swarm-based metaheuristic optimisation method modelled on the predatory behaviour of bald eagles hunting prey. It functions by maintaining a balance between global exploration and local refinement during optimisation tasks, which allows it to produce near-optimal outcomes across diverse problem areas. An extensive overview synthesises recent advancements in this method, detailing its underlying natural concept, operational framework, and subsequent algorithm modifications and hybridisations. Comparative assessments evaluate its effectiveness against other modern optimisation techniques, accompanied by a meta-analysis tracing the evolution of the method. The review maps the practical deployment of the algorithm across varied computational domains while outlining viable directions for future investigation, serving as a consolidated reference for deploying swarm-based intelligence to solve complex optimisation challenges.
Optimisation techniques are essential for finding the most effective solutions to complex computational and engineering challenges. Swarm-based algorithms like Bald Eagle Search mimic biological behaviours to search vast problem spaces efficiently. Understanding how this specific method performs, evolves, and compares with other modern alternatives helps technical practitioners choose the most capable tools for intricate problem-solving across multiple fields.
The abstract highlights broad applications of the Bald Eagle Search algorithm for complex problem-solving across varied domains, but it does not specify concrete commercial products, end users, or specific industrial deployment settings. As an algorithmic review and meta-analysis, the work represents early-stage methodological research rather than a direct commercial tool, leaving the specific path to real-world deployment dependent on application-specific implementations.
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Bald Eagle Search (BES) is a recent and highly successful swarm-based metaheuristic algorithm inspired by the hunting strategy of bald eagles in capturing prey. With its remarkable ability to balance global and local searches during optimization, the BES algorithm effectively addresses various optimization challenges across diverse domains, yielding nearly optimal results. This paper offers a comprehensive review of recent research on BES. Beginning with an introduction to BES's natural inspiration and conceptual optimization framework, it explores modifications, hybridizations, and applications of BES across various domains. Then, a critical evaluation of BES's performance is provided, offering an update on its effectiveness compared to recently published algorithms. Furthermore, the paper presents a meta-analysis of BES developments and outlines potential future research directions. As swarm-inspired metaheuristic algorithms become increasingly important in tackling complex optimization problems, this study is a valuable resource for researchers aiming to understand swarm-based algorithms, mainly focusing on BES comprehensively. It investigates BES's evolution, exploring its potential applications in solving intricate optimization challenges across diverse fields.
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DOI: 10.1080/21642583.2024.2385310
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