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Kitsune Optimiser Algorithm
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  • Fergani Okba,
  • Raihane Mechgoug,
  • Ahmed Bouzid Afulay,
  • * NaciraTkouti,
  • AIT ABBAS Hamou
Fergani Okba
Universite Mohamed Khider de Biskra Departement de Genie Electrique

Corresponding Author:[email protected]

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Raihane Mechgoug
Universite Mohamed Khider de Biskra Laboratoire de Genie Electrique Biskra
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Ahmed Bouzid Afulay
Miskolci Egyetem Vezetestudomanyi Intezet
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* NaciraTkouti
Universite Mohamed Khider de Biskra Laboratoire de Genie Electrique Biskra
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Universite de Bouira
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The Kitsune Optimization Algorithm (KOA), inspired by the mythical Kitsune, introduces a transformative approach in the realm of metaheuristic optimization. This paper presents an in-depth analysis of KOA, underlining its exceptional capabilities in terms of enhanced convergence speed, accuracy, and robustness. Empirical results from 12 benchmark functions along with optimizing power output in Photovoltaic (PV) systems reveal KOA’s rapid convergence capabilities, significantly reducing computational time. In comparison to established algorithms, KOA shows a marked improvement in convergence speed, reaching optimal solutions faster by an average of first 5 iterations. In terms of accuracy, KOA demonstrates an impressive ability to locate global optima with a lower average error margin of 98 %, indicating a substantial increase in solution precision over traditional methods. This level of accuracy is particularly evident in complex multi-modal landscapes, where KOA consistently outperforms its counterparts. Furthermore, KOA exhibits exceptional robustness across various test scenarios, maintaining consistent performance and exhibiting a high level of stability. This robustness is further evidenced in real-world applications, such as the optimization of power output in Photovoltaic (PV) systems, where KOA adapts effectively to dynamic environmental conditions, showcasing its practical applicability and reliability. Overall, the Kitsune Optimization Algorithm sets a new benchmark in the field of metaheuristic algorithms with its enhanced convergence speed, superior accuracy, and robustness, making it a promising tool for tackling complex optimization problems in diverse domains.
07 Feb 2024Submitted to Optimal Control, Applications and Methods
08 Feb 2024Assigned to Editor
08 Feb 2024Submission Checks Completed
08 Feb 2024Review(s) Completed, Editorial Evaluation Pending
08 Feb 2024Reviewer(s) Assigned