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

El-Kenawy, El-Sayed M.; Rizk, Faris H.; Zaki, Ahmed Mohamed; Elshabrawy, Mahmoud; Ibrahim, Abdelhameed; Abdelhamid, Abdelaziz A.; Khodadadi, Nima; ALmetwally, Ehab M.; Eid, Marwa M.

doi:10.21608/jaiep.2024.386694

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

Document Type : Original Article

Authors

¹ 1School 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.

² Computer Science and Intelligent Systems Research Center

³ Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA.

⁴ nComputer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USAon

⁵ Faculty of Engineering, Design and Information & Communications Technology (EDICT), School of ICT, Bahrain Polytechnic, PO Box 33349, Isa Town, Bahrain

⁶ Department of Computer Science, Faculty of Computer and Information Sciences, Ain Shams University

⁷ Department of Civil and Architectural Engineering, University of Miami

⁸ Faculty of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia Faculty of Business Administration, Delta University for Science and Technology, Gamasa 11152, Egypt

⁹ Faculty of Artiﬁcial Intelligence, Delta University for Science and Technology

10.21608/jaiep.2024.386694

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.

Main Subjects