NiOA: A Novel Metaheuristic Algorithm Modeled on the Stealth and Precision of Japanese Ninjas

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.2024.386693

NiOA: A Novel Metaheuristic Algorithm Modeled on the Stealth and Precision of Japanese Ninjas

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 Computer 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.2024.386693

Abstract

This paper presents a new metaheuristic optimization algorithm called the Ninja Optimization Algorithm (NiOA) owing to its characteristics such as stealth, precision, and adaptability of the ninjas of Japan. NiOA is proposed to avoid high exploration and exploitation costs within such complex search spaces and to avoid the problem of getting trapped in local optima. The algorithm imitates ninja searching techniques because it has a scanning phase, adapted to search large areas to look for answers, while the more specific phase is used to refine the answers found. The performance of NiOA is compared with other benchmark optimization functions and some of the frequently used CEC 2005 benchmarks. These benchmarks are well suited to test unimodal and multimodal optimization problems of good quality. Experimental results prove that NiOA can significantly provide better optimization results regarding solution quality, convergence rate, and time complexity, suggesting that NiOA is a robust algorithm for solving high-dimensional large-scale optimization problems. Furthermore, it reveals that NiOA is applicable to solve different kinds of problem spaces, signifying that NiOA can be used in practice on scientific and engineering problems.

Keywords