aspg-28-3253-1792155165 20261016125245 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Artificial Intelligence and Metaheuristics JAIM 2833-5597 2024 8 1 El-Sayed M. El-Kenawy School of ICT, Faculty of Engineering, Design and Information & Communications Technology (EDICT), Bahrain Polytechnic, PO Box 33349, Isa Town, Bahrain; Jadara University Research Center, Jad Faris H. Rizk Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA Ahmed Mohamed Zaki Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA Mahmoud Elshabrawy Mohamed Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA Abdelhameed Ibrahim School of ICT, Faculty of Engineering, Design and Information & Communications Technology (EDICT), Bahrain Polytechnic, PO Box 33349, Isa Town, Bahrain. Abdelaziz A. Abdelhamid Department of Computer Science, Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt; Department of Computer Science, College of Computing and Information Te Nima Khodadadi Department of Civil and Architectural Engineering, University of Miami, Coral Gables, FL, USA Ehab M. Almetwally Department of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia Marwa M. Eid Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura 11152, Egypt In this paper, we propose the Ocotillo Optimization Algorithm (OcOA), a novel desert-inspired metaheuristic designed to solve complex optimization problems. Inspired by the adaptive strategies of desert plants, OcOA aims to achieve a balance between exploration and exploitation in high-dimensional and multimodal search spaces. The algorithm dynamically adjusts its behavior based on feedback from prior iterations, optimizing both search breadth and solution refinement. To evaluate its effectiveness, OcOA was tested against several well-known algorithms on a range of benchmark functions, including unimodal and multimodal functions from the CEC 2005 suite such as Sphere, Rosenbrock, Ackley, and Rastrigin. The results demonstrate that OcOA outperforms competing approaches in terms of accuracy, convergence speed, and computational efficiency. Additionally, its adaptability was validated through feature selection tasks, highlighting its robustness in handling both continuous and discrete optimization challenges. This study positions OcOA as a competitive optimization tool for various real-world applications 2024 39 59 3253 https://creativecommons.org/licenses/by/4.0/ 10.54216/JAIM.080104 https://www.americaspg.com/journal/28/article/3253 https://www.americaspg.com/storage/41729544865.pdf