Cendrawasih Bird Optimization Algorithm : A nature-inspired metaheuristic

Authors

Widi Aribowo
Universitas Negeri Surabaya image/svg+xml
Laith Abualigah
Computer Science Department, Al al-Bayt University, Mafraq 25113, Jordan
Diego Oliva
Depto. de Ingeniería Electro-Fotónica, Universidad de Guadalajara, CUCEI, Guadalajara, México
Abeer Aljohani
Department of Computer Science, Applied College, Taibah University, Madinah, Saudi Arabia
Aliyu Sabo
Department of Electrical and Electronic Engineering, Nigerian Defence Academy, Kaduna, Nigeria
DOI: https://doi.org/10.52549/ijeei.v14i2.7419
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Published: 15-04-2026 • Issue: Vol. 14 No. 2: June 2026 • Section: Regular paper

Abstract

This study proposes a new metaheuristic algorithm named Cendrawasih Bird Optimization Algorithm (COA), inspired by the natural behavior of Cendrawasih birds—an endemic species of Indonesia. COA mimics the main behavior of the birds, namely exploration and exploitation, to create a balance between global exploration and local exploitation capabilities in solving various optimization problems. This algorithm is mathematically modeled and evaluated using 23 benchmark functions from CEC 2017, and its performance is compared against eight state-of-the-art metaheuristic algorithms. The results of numerical experiments, convergence analysis, and statistical tests show that COA has competitive performance, especially in terms of stability and consistency on unimodal and multimodal functions. Overall, COA is proven to be a reliable, robust, and efficient optimization tool with broad application potential in various engineering and scientific fields.
Keywords: Cendrawasih Bird; Optimization Algorithm; Metaheuristic; Swarm intelligence; Global optimization

References

J. Kokina, S. Blanchette, T. H. Davenport, and D. Pachamanova, “Challenges and opportunities for artificial intelligence in auditing: Evidence from the field,†International Journal of Accounting Information Systems, vol. 56, p. 100734, 2025.

A. Mukhtar, P. M. Tiwari, S. Alotaibi, T. Alzahrani, B. Namomsa, and M. Ahmed, “Optimal design of tilt integral derivative controller for a boost converter based on swarm-inspired algorithms,†Scientific Reports, vol. 15, no. 1, 2025. doi:10.1038/s41598-024-84088-7.

L. Zhai, Y. Wang, S. Cui, and Y. Zhou, “A Comprehensive Review of Deep Learning-Based Real-World Image Restoration,†IEEE Access, 2023. doi:10.1109/ACCESS.2023.3250616.

S. Hamouche, N. Rofa, and A. Parent-Lamarche, “Systematic bibliometric review of artificial intelligence in human resource development: Insights for HRD researchers, practitioners and policymakers,†European Journal of Training and Development, vol. 49, no. 1–2, pp. 43–62, 2025. doi:10.1108/EJTD-10-2023-0152.

I. J. Akpan, Y. M. Kobara, J. Owolabi, A. A. Akpan, and O. F. Offodile, “Conversational and generative artificial intelligence and human–chatbot interaction in education and research,†International Transactions in Operational Research, vol. 32, no. 3, pp. 1251–1281, 2025.

M. J. Santamaría Ruiz, C. E. Jiménez Calderón, A. R. Osorio Venecia, A. A. Santodomingo, and M. P. Forero, “Bibliometric behavior of artificial intelligence and digital marketing sustainability,†Procedia Computer Science, pp. 1047–1052, 2025. doi:10.1016/j.procs.2025.03.136.

H. Taherdoost, T.-V. Le, and K. Slimani, “Cryptographic Techniques in Artificial Intelligence Security: A Bibliometric Review,†Cryptography, vol. 9, no. 1, 2025. doi:10.3390/cryptography9010017.

W. Aribowo, L. Abualigah, D. Oliva, and A. Prapanca, “A novel modified mountain gazelle optimizer for tuning parameter proportional integral derivative of DC motor,†Bulletin of Electrical Engineering and Informatics, vol. 13, no. 2, pp. 745–752, 2024.

S. Szénási and G. Légrádi, “Machine learning aided metaheuristics: A comprehensive review of hybrid local search methods,†Expert Systems with Applications, vol. 258, p. 125192, 2024. doi:10.1016/j.eswa.2024.125192.

B. Brahim, M. Kobayashi, M. Al Ali, T. Khatir, and M. E. A. E. Elmeliani, “Metaheuristic Optimization Algorithms: An Overview,†HCMCOU Journal of Science: Computing and Structure, 2024.

A. Susanti et al., “Application of the Orangutan Optimization Algorithm for Solving Vehicle Routing Problems in Sustainable Transportation Systems,†Engineering, Technology & Applied Science Research, vol. 15, no. 3, pp. 22915–22922, 2025. doi:10.48084/etasr.10545.

W. Aribowo, “Comparison Study on Economic Load Dispatch Using Metaheuristic Algorithm,†Gazi University Journal of Science, vol. 35, no. 1, 2022. doi:10.35378/gujs.820805.

M. Liu, Y. Yuan, A. Xu, T. Deng, and L. Jian, “A learning-based artificial bee colony algorithm for operation optimization in gas pipelines,†Information Sciences, vol. 690, p. 121593, 2025.

M. A. Awadallah et al., “Multi-objective ant colony optimization,†Archives of Computational Methods in Engineering, vol. 32, no. 2, pp. 995–1037, 2025.

M. Premalatha, M. Jayasudha, R. Čep, J. Priyadarshini, K. Kalita, and P. Chatterjee, “A comparative evaluation of nature-inspired algorithms for feature selection problems,†Heliyon, vol. 10, no. 1, p. e23571, 2024. doi:10.1016/j.heliyon.2023.e23571.

W. J. Ramadhan, A. Amirullah, and A. S. Wardhana, “Telegram Application to Monitor and Control of Automatic Railway Crossing Prototype Using Automatic Transfer Switch,†Vokasi Unesa Bulletin of Engineering Technology and Applied Science, vol. 1, no. 2, pp. 54–63, 2024. doi:10.26740/vubeta.v1i2.35391.

R. R. Tan, M. V. Migo-Sumagang, and K. B. Aviso, “Recent trends in optimization models for industrial decarbonization,†Current Opinion in Chemical Engineering, vol. 48, p. 101118, 2025.

A. Ramos, V. G. Angel, M. Siqueiros, T. Sahagun, L. Gonzalez, and R. Ballesteros, “Reviewing additive manufacturing techniques: Material trends and weight optimization possibilities through innovative printing patterns,†Materials, vol. 18, no. 6, p. 1377, 2025.

O. L. Abraham, M. A. Ngadi, J. B. M. Sharif, and M. K. M. Sidik, “Multi-objective optimization techniques in cloud task scheduling: A systematic literature review,†IEEE Access, 2025.

H. N. Fakhouri, F. M. Awaysheh, S. Alawadi, M. Alkhalaileh, and F. Hamad, “Four vector intelligent metaheuristic for data optimization,†Computing, vol. 106, no. 7, pp. 2321–2359, 2024.

D. E. Goldberg and J. H. Holland, “Genetic Algorithms and Machine Learning,†1988. doi:10.1023/A:1022602019183.

J. Kennedy and R. Eberhart, “Particle Swarm Optimization,†Proceedings of the IEEE International Conference on Neural Networks, pp. 1942–1948, 1995.

M. Dorigo, M. Birattari, and T. Stutzle, “Ant Colony Optimization,†IEEE Computational Intelligence Magazine, vol. 1, no. 4, pp. 28–39, 2007.

N. A. Mansour, M. S. Saraya, and A. I. Saleh, “Groupers and Moray Eels (GME) Optimization: A Nature-Inspired Metaheuristic Algorithm for Solving Complex Engineering Problems,†Neural Computing and Applications, vol. 37, no. 1, pp. 63–90, 2025. doi:10.1007/s00521-024-10384-y.

J. O. Agushaka, A. E. Ezugwu, and L. Abualigah, “Dwarf Mongoose Optimization Algorithm,†Computer Methods in Applied Mechanics and Engineering, 2022. doi:10.1016/j.cma.2022.114570.

A. Faramarzi, M. Heidarinejad, S. Mirjalili, and A. H. Gandomi, “Marine Predators Algorithm: A Nature-Inspired Metaheuristic,†Expert Systems with Applications, vol. 152, p. 113377, 2020. doi:10.1016/j.eswa.2020.113377.

B. Abdollahzadeh, F. S. Gharehchopogh, N. Khodadadi, and S. Mirjalili, “Mountain Gazelle Optimizer: A New Nature-Inspired Metaheuristic Algorithm for Global Optimization Problems,†Advances in Engineering Software, vol. 174, p. 103282, 2022.

J. Kennedy and R. Eberhart, “Particle Swarm Optimization,†Proceedings of ICNN’95 – International Conference on Neural Networks, IEEE, pp. 1942–1948, 1995.

L. Abualigah, D. Yousri, M. Abd Elaziz, A. A. Ewees, M. A. A. Al-Qaness, and A. H. Gandomi, “Aquila Optimizer: A Novel Meta-Heuristic Optimization Algorithm,†Computers & Industrial Engineering, vol. 157, p. 107250, 2021.

H. A. Shehadeh, “Chernobyl Disaster Optimizer (CDO): A Novel Meta-Heuristic Method for Global Optimization,†Neural Computing and Applications, vol. 35, no. 15, pp. 10733–10749, 2023.

M. Braik and H. Al-Hiary, “Rüppell’s Fox Optimizer: A Novel Meta-Heuristic Approach for Solving Global Optimization Problems,†Cluster Computing, vol. 28, no. 5, pp. 1–77, 2025.

S. Zhao, T. Zhang, S. Ma, and M. Wang, “Sea-Horse Optimizer: A Novel Nature-Inspired Meta-Heuristic for Global Optimization Problems,†Applied Intelligence, pp. 1–28, 2022. doi:10.1007/s10489-022-03994-3.

H. A. Shehadeh, “Bermuda Triangle Optimizer (BTO): A Novel Metaheuristic Method for Global Optimization,†International Journal of Advanced Soft Computing Applications, vol. 17, no. 2, 2025. doi:10.15849/IJASCA.250730.01.

S. O. Oladejo, S. O. Ekwe, and S. Mirjalili, “The Hiking Optimization Algorithm: A Novel Human-Based Metaheuristic Approach,†Knowledge-Based Systems, vol. 296, p. 111880, 2024.

M.-Y. Cheng and M. N. Sholeh, “Artificial Satellite Search: A New Metaheuristic Algorithm for Optimizing Truss Structure Design and Project Scheduling,†Applied Mathematical Modelling, vol. 143, 2025. doi:10.1016/j.apm.2025.116008.

S. Zhao, T. Zhang, L. Cai, and R. Yang, “Triangulation Topology Aggregation Optimizer: A Novel Mathematics-Based Meta-Heuristic Algorithm for Continuous Optimization and Engineering Applications,†Expert Systems with Applications, vol. 238, 2024. doi:10.1016/j.eswa.2023.121744.

R.-B. Wang et al., “The Animated Oat Optimization Algorithm: A Nature-Inspired Metaheuristic for Engineering Optimization and a Case Study on Wireless Sensor Networks,†Knowledge-Based Systems, vol. 318, 2025. doi:10.1016/j.knosys.2025.113589.

M. Ghasemi, M. Zare, A. Zahedi, M.-A. Akbari, S. Mirjalili, and L. Abualigah, “Geyser Inspired Algorithm: A New Geological-Inspired Meta-Heuristic for Real-Parameter and Constrained Engineering Optimization,†Journal of Bionic Engineering, vol. 21, no. 1, pp. 374–408, 2024. doi:10.1007/s42235-023-00437-8.

A. T. Mozhdehi et al., “Divine Religions Algorithm: A Novel Social-Inspired Metaheuristic Algorithm for Engineering and Continuous Optimization Problems,†Cluster Computing, vol. 28, no. 4, 2025. doi:10.1007/s10586-024-04954-x.

S. Fu, K. Li, H. Huang, C. Ma, Q. Fan, and Y. Zhu, “Red-Billed Blue Magpie Optimizer: A Novel Metaheuristic Algorithm for 2D/3D UAV Path Planning and Engineering Design Problems,†Artificial Intelligence Review, vol. 57, no. 6, 2024. doi:10.1007/s10462-024-10716-3.

M. Azizi, M. Baghalzadeh Shishehgarkhaneh, M. Basiri, R. C. Moehler, Y. Fang, and M. Chan, “Wolf-Bird Optimizer (WBO): A Novel Metaheuristic Algorithm for Building Information Modeling-Based Resource Tradeoff,†Journal of Engineering Research, vol. 13, no. 2, pp. 763–785, 2025. doi:10.1016/j.jer.2023.11.024.

C. Yuan, D. Zhao, A. A. Heidari, L. Liu, Y. Chen, and H. Chen, “Polar Lights Optimizer: Algorithm and Applications in Image Segmentation and Feature Selection,†Neurocomputing, vol. 607, 2024. doi:10.1016/j.neucom.2024.128427.

A. Rodan, A.-K. Al-Tamimi, L. Al-Alnemer, S. Mirjalili, and P. Tiňo, “Enzyme Action Optimizer: A Novel Bio-Inspired Optimization Algorithm,†Journal of Supercomputing, vol. 81, no. 5, 2025. doi:10.1007/s11227-025-07052-w.

X. Wang, V. Snášel, S. Mirjalili, J.-S. Pan, L. Kong, and H. A. Le Thi, “Artificial Protozoa Optimizer: A Novel Bio-Inspired Metaheuristic Algorithm for Engineering Applications,†Knowledge-Based Systems, 2024.

M. Braik et al., “Coyote and Badger Optimization: A Novel Nature-Inspired Metaheuristic Algorithm for Solving Engineering Optimization Problems,†Expert Systems with Applications, 2025.

H. Yao, S. Wang, W. Wang, J. Sun, F. Long, and H. A. Abbass, “Parallel Human Evolutionary Optimization Algorithm,†Applied Soft Computing, vol. 145, 2024.

R. Khodadadi, M. Mafarja, L. Abualigah, M. Abd Elaziz, and S. Mirjalili, “Perfumer Optimization Algorithm,†Artificial Intelligence Review, 2025.

M. Braik, M. Mafarja, and M. Alkhraisat, “Target Depth Search Strategy Embedded in Arctic Puffin Optimization,†Scientific Reports, vol. 14, 2024.

M. Ahmadianfar, R. Mohamed, and M. K. Sangaiah, “Fungal Growth Optimizer: A Novel Nature-Inspired Metaheuristic Algorithm,†Knowledge-Based Systems, vol. 314, 2025.

S. A. Elsayed, M. A. M. Abdel-Basset, and H. M. Zawbaa, “Elk Herd Optimizer: A Novel Metaheuristic Algorithm,†Applied Soft Computing, vol. 145, 2024.

T. Ramdani et al., “Builder Optimization Algorithm: An Efficient Human-Inspired Metaheuristic Algorithm for Solving Optimization Problems,†Applied Intelligence, 2025.

H. Zhang et al., “Catch Fish Optimization Algorithm: A New Human-Based Metaheuristic Approach for Optimization,†Engineering Applications of Artificial Intelligence, vol. 133, 2024.

S. Hedayat et al., “Paper Publishing Based Optimization: A New Human-Based Metaheuristic Algorithm for Solving Engineering Problems,†Neural Computing and Applications, 2025.

Author Biographies

Laith Abualigah
Depto. de Ingeniería Electro-Fotónica, Universidad de Guadalajara, CUCEI, Guadalajara, México

How to Cite

[1]
W. Aribowo, L. Abualigah, D. Oliva, A. Aljohani, and A. Sabo, “Cendrawasih Bird Optimization Algorithm : A nature-inspired metaheuristic”, IJEEI, vol. 14, no. 2, pp. 363–375, Apr. 2026, doi: 10.52549/ijeei.v14i2.7419.