aspg-18-3691-1792524139 20261020192219 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Intelligent Systems and Internet of Things JISIoT 2769-786X 2690-6791 2025 16 2 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; Applied Science Research Center. Applie Amel Ali Alhussan Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia Doaa Sami Khafaga Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia Amal H. Alharbi Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia Sarah A. Alzakari Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia 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 Abdelhameed Ibrahim School of ICT, Faculty of Engineering, Design and Information, Communications Technology (EDICT), Bahrain Polytechnic, PO Box 33349, Isa Town, Bahrain Marwa M. Eid Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura 11152, Egypt The Comment Feedback Optimization Algorithm (CFOA) presented a novel feedback-driven model for solving optimization problems, incorporating ideas based on positive and negative feedback loops. Unlike other optimization algorithms, CFOA includes feedback adjustments for better tuning the exploration-exploitation trade-off, thus making CFOA less sensitive to the dimensions of problems and their nonlinearity. Some proposed features include feedback dynamics for adaptive search options, parameter control by a decay function, and mechanisms for escaping local optima. CFOA’s performance has been benchmarked on CEC 2005 test cases with many evaluations. The results demonstrate better convergence speed, solution quality, and computational complexity compared with the Sine Cosine Algorithm (SCA), Gravitational Search Algorithm (GSA), and Tunicate Swarm Algorithm (TSH). The efficiency of the approach used by CFOA makes it an indispensable tool for solving real-world optimization problems across various application domains such as machine learning, engineering, and logistics. 2025 123 141 3691 https://creativecommons.org/licenses/by/4.0/ 10.54216/JISIoT.160210 https://www.americaspg.com/journal/18/article/3691 https://www.americaspg.com/storage/11741866653.pdf