In recent times, there has been notable progress in control systems across various industrial domains, necessitating effective management of dynamic systems for optimal functionality. A crucial research focus has emerged in optimizing control parameters to augment controller performance. Among the plethora of optimization algorithms, the mountain gazelle optimizer (MGO) stands out for its capacity to emulate the agile movements and behavioral strategies observed in mountain gazelles. This paper introduces a novel approach employing MGO to optimize control parameters in both a DC motor and three-tank liquid level systems. The fine-tuning of proportional-integral-derivative (PID) controller parameters using MGO achieves remarkable results, including a rise time of 0.0478 s, zero overshoot, and a settling time of 0.0841 s for the DC motor system. Similarly, the liquid level system demonstrates improved control with a rise time of 11.0424 s and a settling time of 60.6037 s. Comparative assessments with competitive algorithms, such as the grey wolf optimizer and particle swarm optimization, reveal MGO's superior performance. Furthermore, a new performance indicator, ZLG, is introduced to comprehensively evaluate control quality. The MGO-based approach consistently achieves lower ZLG values, showcasing its adaptability and robustness in dynamic system control and parameter optimization. By providing a dependable and efficient optimization methodology, this research contributes to advancing control systems, promoting stability, and enhancing efficiency across diverse industrial applications.

Applied Science Research Center Applied Science Private University Amman 11931 Jordan

Centre for Research Impact and Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura 140401 Punjab India

Computer Science Department Al al Bayt University Mafraq 25113 Jordan

Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602105 India

Department of Computer Engineering Batman University Batman 72100 Turkey

Department of Computer Science and Engineering College of Applied Studies and Community Service King Saud University Riyadh 11362 Saudi Arabia

Department of Mathematics Facility of Science The Hashemite University P O box 330127 Zarqa 13133 Jordan

Faculty of Educational Sciences Al Ahliyya Amman University Amman 19328 Jordan

Faculty of Electrical Engineering and Computer Science VŠB Technical University of Ostrava Poruba Ostrava 70800 Czech Republic

Faculty of Engineering and Computing Liwa College Abu Dhabi United Arab Emirates

Research Center Mazaya University College Nasiriyah Iraq

Unit for Data Science and Computing North West University 11 Hofman Street Potchefstroom 2520 South Africa

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