iHow Optimization Algorithm: A Human-Inspired Metaheuristic Approach for Complex Problem Solving and Feature Selection
El-Kenawy, E., Rizk, F., Zaki, A., Elshabrawy, M., Ibrahim, A., Abdelhamid, A., Khodadadi, N., ALmetwally, E., Eid, M. (2024). iHow Optimization Algorithm: A Human-Inspired Metaheuristic Approach for Complex Problem Solving and Feature Selection. EKB Journal Management System, 1(2), 36-53. doi: 10.21608/jaiep.2024.386694
El-Sayed M. El-Kenawy; Faris H. Rizk; Ahmed Mohamed Zaki; Mahmoud Elshabrawy; Abdelhameed Ibrahim; Abdelaziz A. Abdelhamid; Nima Khodadadi; Ehab M. ALmetwally; Marwa M. Eid. "iHow Optimization Algorithm: A Human-Inspired Metaheuristic Approach for Complex Problem Solving and Feature Selection". EKB Journal Management System, 1, 2, 2024, 36-53. doi: 10.21608/jaiep.2024.386694
El-Kenawy, E., Rizk, F., Zaki, A., Elshabrawy, M., Ibrahim, A., Abdelhamid, A., Khodadadi, N., ALmetwally, E., Eid, M. (2024). 'iHow Optimization Algorithm: A Human-Inspired Metaheuristic Approach for Complex Problem Solving and Feature Selection', EKB Journal Management System, 1(2), pp. 36-53. doi: 10.21608/jaiep.2024.386694
El-Kenawy, E., Rizk, F., Zaki, A., Elshabrawy, M., Ibrahim, A., Abdelhamid, A., Khodadadi, N., ALmetwally, E., Eid, M. iHow Optimization Algorithm: A Human-Inspired Metaheuristic Approach for Complex Problem Solving and Feature Selection. EKB Journal Management System, 2024; 1(2): 36-53. doi: 10.21608/jaiep.2024.386694

iHow Optimization Algorithm: A Human-Inspired Metaheuristic Approach for Complex Problem Solving and Feature Selection

Journal of Artificial Intelligence in Engineering Practice
Volume 1, Issue 2, November 2024, Page 36-53  XML PDF (1.03 MB)
Document Type: Original Article
DOI: 10.21608/jaiep.2024.386694
View on SCiNiTO View on SCiNiTO
Authors
El-Sayed M. El-Kenawy email 1; Faris H. Rizk2; Ahmed Mohamed Zakiorcid 3; Mahmoud Elshabrawy4; Abdelhameed Ibrahimorcid 5; Abdelaziz A. Abdelhamid6; Nima Khodadadi7; Ehab M. ALmetwallyorcid 8; Marwa M. Eid9
11School of ICT, Faculty of Engineering, Design and Information & Communications Technology (EDICT), Bahrain Polytechnic, PO Box 33349, Isa Town, Bahrain. 2Jadara University Research Center, Jadara University, Jordan. 3Applied Science Research Center. Applied Science Private University, Amman, Jordan. 4Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt.
2Computer Science and Intelligent Systems Research Center
3Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA.
4nComputer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USAon
5Faculty of Engineering, Design and Information & Communications Technology (EDICT), School of ICT, Bahrain Polytechnic, PO Box 33349, Isa Town, Bahrain
6Department of Computer Science, Faculty of Computer and Information Sciences, Ain Shams University
7Department of Civil and Architectural Engineering, University of Miami
8Faculty of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia Faculty of Business Administration, Delta University for Science and Technology, Gamasa 11152, Egypt
9Faculty of Artificial Intelligence, Delta University for Science and Technology
Abstract
In this paper, we propose the iHow Optimization Algorithm (iHowOA), a novel metaheuristic algorithm inspired by human-like cognitive processes such as learning, knowledge acquisition, and experience-based decision-making. The iHowOA aims to enhance the exploration-exploitation balance inherent to solving complex optimization problems by mimicking how humans gather data, learn, and improve over time. We tested the algorithm on standard benchmark functions, including those from the CEC 2005 suite, to evaluate its performance in terms of convergence, computational efficiency, and solution accuracy. Furthermore, the Binary iHowOA (biHowOA) was employed for feature selection tasks, and its performance was compared with other popular optimization algorithms. The results show that iHowOA achieves superior performance, consistently finding optimal solutions while maintaining computational efficiency. The biHowOA also demonstrated strong capability in feature selection, providing reduced feature sets with minimal classification error. Our experiments confirm that iHowOA offers an effective solution for both continuous optimization and feature selection
challenges. 
Statistics
Article View: 326
PDF Download: 429