Kakapo Optimization Algorithm (KOA): A Novel Bio-inspired Metaheuristic for Optimization Applications
Qawaqneh H. Alomari K.M. Alomari S. Bektemyssova G. Smerat A. Montazeri Z. Dehghani M. Malik O.P. Eguchi K.
31 December 2025Intelligent Network and Systems Society
International Journal of Intelligent Engineering and Systems
2025#18Issue 11913 - 929 pp.
Kakapo Optimization Algorithm (KOA), a novel bio-inspired metaheuristic grounded in the unique ecological and behavioural characteristics of the endangered kakapo parrot is presented. Unlike flighted birds, kakapos rely on nocturnal roaming, camouflage, booming calls, and cautious foraging strategies for survival. These natural mechanisms are systematically translated into mathematical operators to achieve the critical optimization balance between global exploration and local exploitation. The exploration process is modelled on the kakapo’s zigzag nocturnal navigation, ensuring broad coverage of the search space and preventing premature convergence. The exploitation phase is inspired by freezing and camouflage behaviours, introducing controlled local refinements to intensify promising regions while maintaining solution stability. A survival-based elitist selection further preserves high-quality solutions, sustaining convergence reliability across iterations. KOA has been extensively validated on the CEC 2017 benchmark suite, encompassing unimodal, multimodal, hybrid, and composition functions. Comparative analysis against recent metaheuristic algorithms demonstrates KOA’s robust adaptability, consistent accuracy, and high stability, as confirmed through statistical metrics and boxplot visualizations. Results indicate that KOA effectively converges toward global optimum with low variance, outperforming or matching state-of-the-art algorithms across diverse problem landscapes. The biologically grounded design of KOA, its strong exploratory and exploitative dynamics, and its reliable convergence highlight its potential for real-world applications such as engineering design, scheduling, energy optimization, and machine learning hyperparameter tuning. This article is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. License details: https://creativecommons.org/licenses/by-sa/4.0/
CEC 2017 benchmarks , Exploration and exploitation , Global optimization , Kakapo optimization algorithm , Metaheuristic , Nature-inspired computing
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Department of Mathematics, Al Zaytoonah University of Jordan, Amman, 11733, Jordan
Faculty of Information Technology, Abu Dhabi University, United Arab Emirates
Faculty of Science and Information Technology, Software Engineering, Jadara University, Irbid, 21110, Jordan
Department of Computer Engineering, International Information Technology University, Almaty, 050000, Kazakhstan
Faculty of Educational Sciences, Al-Ahliyya Amman University, Amman, 19328, Jordan
Department of Biosciences, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, 602105, India
Centre for Research Impact and Outcome, Chitkara University, Punjab, India
Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, 7155713876, Iran
Department of Electrical and Software Engineering, University of Calgary, Calgary, T2N 1N4, AB, Canada
Department of Information Electronics, Fukuoka Institute of Technology, Japan
Department of Mathematics
Faculty of Information Technology
Faculty of Science and Information Technology
Department of Computer Engineering
Faculty of Educational Sciences
Department of Biosciences
Centre for Research Impact and Outcome
Department of Electrical and Electronics Engineering
Department of Electrical and Software Engineering
Department of Information Electronics
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