1. Pan JS, Hu P, Snášel V, Chu SC. A survey on binary metaheuristic algorithms and their engineering applications. Artificial Intelligence Review. 2023;56(7):6101-67. https://doi.org/10.1007/s10462-022-10328-9.
2. Zhao S, Zhang T, Ma S, Chen M. Dandelion optimizer: a nature-inspired metaheuristic algorithm for engineering applications. Engineering Applications of Artificial Intelligence. 2022;114:105075. https://doi.org/10.1016/j.engappai.2022.105075.
3. Kashani AR, Camp CV, Rostamian M, Azizi K, Gandomi AH. Population-based optimization in structural engineering: a review. Artificial Intelligence Review. 2022;55(1):345-452. https://doi.org/10.1007/s10462-021-10036-w.
4. Lameesa A, Hoque M, Alam MSB, Ahmed SF, Gandomi AH. Role of metaheuristic algorithms in healthcare: a comprehensive investigation across clinical diagnosis, medical imaging, operations management, and public health. Journal of Computational Design and Engineering. 2024;11(3):223-47. https://doi.org/10.1093/jcde/qwae046.
5. Bertsimas D, Tsitsiklis JN. Introduction to linear optimization. Athena Scientific; 1997.
6. Daoud MS, Shehab M, Al-Mimi HM, Abualigah L, Zitar RA, Shambour MKY. Gradient-based optimizer (GBO): a review, theory, variants, and applications. Archives of Computational Methods in Engineering. 2023;30(4):2431-49. https://doi.org/10.1007/s11831-022-09872-y.
7. Mirjalili S, Lewis A. The whale optimization algorithm. Advances in Engineering Software. 2016;95:51-67. https://doi.org/10.1016/j.advengsoft.2016.01.008.
8. Ahmadianfar I, Heidari AA, Gandomi AH, Chu X, Chen H. RUN beyond the metaphor: an efficient optimization algorithm based on Runge Kutta method. Expert Systems with Applications. 2021;181:115079. https://doi.org/10.1016/j.eswa.2021.115079.
9. Sowmya R, Premkumar M, Jangir P. Newton-Raphson-based optimizer: a new population-based metaheuristic algorithm for continuous optimization problems. Engineering Applications of Artificial Intelligence. 2024;128:107532. https://doi.org/10.1016/j.engappai.2023.107532.
10. Oladejo SO, Ekwe SO, Mirjalili S. The hiking optimization algorithm: a novel human-based metaheuristic approach. Knowledge-Based Systems. 2024;296:111880. https://doi.org/10.1016/j.knosys.2024.111880.
11. Ehsaeyan E. Rock-climbing group: an innovative meta-heuristic approach for efficiently tackling optimization problems. International Journal of Engineering Transactions B: Applications. 2025;38(11):2796-818. https://doi.org/10.5829/ije.2025.38.11b.24.
12. Ehsaeyan E. Gold seekers algorithm: an innovative metaheuristic approach for global optimization and its application in image segmentation. International Journal of Engineering Transactions C: Aspects. 2025;38(9):2114-29. https://doi.org/10.5829/ije.2025.38.09c.09.
13. Verij Kazemi M, Fazeli Veysari E. A new optimization algorithm inspired by the quest for the evolution of human society: human felicity algorithm. Expert Systems with Applications. 2022;193:116468. https://doi.org/10.1016/j.eswa.2021.116468.
14. Oladejo SO, Ekwe SO, Akinyemi LA, Mirjalili SA. The deep sleep optimizer: a human-based metaheuristic approach. IEEE Access. 2023;11:83639-65. https://doi.org/10.1109/ACCESS.2023.3298105.
15. Feng X, Zou R, Yu H. A novel optimization algorithm inspired by the creative thinking process. Soft Computing. 2015;19(10):2955-72. https://doi.org/10.1007/s00500-014-1459-6.
16. Kahrizi MR, Kabudian SJ. Projectiles optimization: a novel metaheuristic algorithm for global optimization. International Journal of Engineering Transactions A: Basics. 2020;33(10):1924-38. https://doi.org/10.5829/ije.2020.33.10a.11.
17. El-Shorbagy MA, Bouaouda A, Nabwey HA, Abualigah L, Hashim FA. Advances in Henry gas solubility optimization: a physics-inspired metaheuristic algorithm with its variants and applications. IEEE Access. 2024;12:26062-95. https://doi.org/10.1109/ACCESS.2024.3365700.
18. Azizi M. Atomic orbital search: a novel metaheuristic algorithm. Applied Mathematical Modelling. 2021;93:657-83. https://doi.org/10.1016/j.apm.2020.12.021.
19. Mahdavi-Meymand A, Zounemat-Kermani M. Homonuclear molecules optimization (HMO) meta-heuristic algorithm. Knowledge-Based Systems. 2022;258:110032. https://doi.org/10.1016/j.knosys.2022.110032.
20. Alba E, Dorronsoro B. Introduction to cellular genetic algorithms. Cellular Genetic Algorithms. 2008:3-20. https://doi.org/10.1007/978-0-387-77610-1_1.
21. Cheng MY, Prayogo D. Symbiotic organisms search: a new metaheuristic optimization algorithm. Computers & Structures. 2014;139:98-112. https://doi.org/10.1016/j.compstruc.2014.03.007.
22. Saremi S, Mirjalili S, Lewis A. Grasshopper optimisation algorithm: theory and application. Advances in Engineering Software. 2017;105:30-47. https://doi.org/10.1016/j.advengsoft.2017.01.004.
23. Talatahari S, Azizi M, Tolouei M, Talatahari B, Sareh P. Crystal structure algorithm (CryStAl): a metaheuristic optimization method. IEEE Access. 2021;9:71244-61. https://doi.org/10.1109/ACCESS.2021.3079161.
24. Zhang Q, Gao H, Zhan ZH, Li J, Zhang H. Growth optimizer: a powerful metaheuristic algorithm for solving continuous and discrete global optimization problems. Knowledge-Based Systems. 2023;261:110206. https://doi.org/10.1016/j.knosys.2022.110206.
25. Han M, Du Z, Yuen KF, Zhu H, Li Y, Yuan Q. Walrus optimizer: a novel nature-inspired metaheuristic algorithm. Expert Systems with Applications. 2024;239:122413. https://doi.org/10.1016/j.eswa.2023.122413.
26. Abdollahzadeh B, Khodadadi N, Barshandeh S, Trojovský P, Gharehchopogh FS, El-Kenawy ESM, et al. Puma optimizer (PO): a novel metaheuristic optimization algorithm and its application in machine learning. Cluster Computing. 2024;27(4):5235-83. https://doi.org/10.1007/s10586-023-04221-5.
27. Zhong C, Li G, Meng Z, Li H, Yildiz AR, Mirjalili S. Starfish optimization algorithm (SFOA): a bio-inspired metaheuristic algorithm for global optimization compared with 100 optimizers. Neural Computing and Applications. 2025;37(5):3641-83. https://doi.org/10.1007/s00521-024-10694-1.
28. Lara-Montaño OD, Gómez-Castro FI, Gutiérrez-Antonio C, Dragoi EN. Success-based optimization algorithm (SBOA): development and enhancement of a metaheuristic optimizer. Computers & Chemical Engineering. 2025;194:108987. https://doi.org/10.1016/j.compchemeng.2024.108987.
29. Amani B, Nouri M, Mousavi Ghasemi SA. Gray squirrel foraging algorithm for function optimization. International Journal of Engineering Transactions A: Basics. 2026;39(7):1644-56. https://doi.org/10.5829/ije.2026.39.07a.09.
30. Gandomi AH, Yang XS, Alavi AH. Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Engineering with Computers. 2013;29(1):17-35. https://doi.org/10.1007/s00366-011-0241-y.
31. Falistocco E. The millenary history of the fig tree (Ficus carica L.). Advances in Agriculture Horticulture and Entomology. 2020;5:130. https://doi.org/10.37722/AAHAE.202051.
32. Houssein EH, Saad MR, Hashim FA, Shaban H, Hassaballah M. Lévy flight distribution: a new metaheuristic algorithm for solving engineering optimization problems. Engineering Applications of Artificial Intelligence. 2020;94:103731. https://doi.org/10.1016/j.engappai.2020.103731.
33. Singh S. Critical reasons for crashes investigated in the National Motor Vehicle Crash Causation Survey. National Highway Traffic Safety Administration; 2018. Report No.: DOT HS 812 506.
34. Liu Y, Liang Z, Zhong W, Xue Y, Wang Y, Tao N, et al. Multi-objective predictive cruise control for electric heavy-duty trucks considering fleet battery swapping under cyber-physical system. Energy. 2025;321:135462. https://doi.org/10.1016/j.energy.2025.135462.
35. Yu L, Wang R. Researches on adaptive cruise control system: a state of the art review. Proceedings of the Institution of Mechanical Engineers Part D: Journal of Automobile Engineering. 2021;236(2-3):211-40. https://doi.org/10.1177/09544070211019254.
36. Heybetli F, Danayiyen Y, Taşdemir AB, Şenyiğit Ş. Comparative analysis of metaheuristic algorithms in PID-based vehicle cruise control systems. Verus Journal. 2025;25(1):1-16. https://doi.org/10.5152/electrica.2025.25051.
37. Van Keulen T, Naus G, De Jager B, Van De Molengraft R, Steinbuch M, Aneke E. Predictive cruise control in hybrid electric vehicles. World Electric Vehicle Journal. 2009;3(1):494-504.