ArticleScientific reports2022
The cheetah optimizer: a nature-inspired metaheuristic algorithm for large-scale optimization problems.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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36 citing papers in PubMed.
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- Design of a liquid cooled battery thermal management system using neural networks, cheetah optimizer and salp swarm algorithm.Scientific reports · 2025Article
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- Basketball team optimization algorithm (BTOA): a novel sport-inspired meta-heuristic optimizer for engineering applications.Scientific reports · 2025Article
- Enhancing stochastic optimal power flow with modified cheetah optimizer for integrating renewable energy sources.Scientific reports · 2025Article
- A novel approach to intrusion detection system using hybrid flower pollination and cheetah optimization algorithm.Scientific reports · 2025Article
- Adaptive image encryption approach using an enhanced swarm intelligence algorithm.Scientific reports · 2025Article
- Simulating online and offline tasks using hybrid cheetah optimization algorithm for patients affected by neurodegenerative diseases.Scientific reports · 2025Article
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- Carrion in Bomas: Multiple Observations of Cheetah (Ecology and evolution · 2025Article
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5 authors.
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Abstract
Motivated by the hunting strategies of cheetahs, this paper proposes a nature-inspired algorithm called the cheetah optimizer (CO). Cheetahs generally utilize three main strategies for hunting prey, i.e., searching, sitting-and-waiting, and attacking. These strategies are adopted in this work. Additionally, the leave the pray and go back home strategy is also incorporated in the hunting process to improve the proposed framework's population diversification, convergence performance, and robustness. We perform intensive testing over 14 shifted-rotated CEC-2005 benchmark functions to evaluate the performance of the proposed CO in comparison to state-of-the-art algorithms. Moreover, to test the power of the proposed CO algorithm over large-scale optimization problems, the CEC2010 and the CEC2013 benchmarks are considered. The proposed algorithm is also tested in solving one of the well-known and complex engineering problems, i.e., the economic load dispatch problem. For all considered problems, the results are shown to outperform those obtained using other conventional and improved algorithms. The simulation results demonstrate that the CO algorithm can successfully solve large-scale and challenging optimization problems and offers a significant advantage over different standards and improved and hybrid existing algorithms. Note that the source code of the CO algorithm is publicly available at https://www.optim-app.com/projects/co .
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