Abstract
This research review article provides a comprehensive examination of optimization techniques in systems engineering, highlighting their pivotal role in enhancing system performance, efficiency, and problem-solving capabilities. Through a structured exploration encompassing theoretical frameworks, methodologies, applications, and significant findings, the article synthesizes current knowledge and advancements in the field. It delves into various optimization methods, including traditional linear and nonlinear programming, alongside emerging trends such as swarm intelligence, nature-inspired algorithms, and the integration of machine learning. Case studies and recent research findings underscore the practical implications and effectiveness of these techniques across diverse engineering challenges. The review identifies key insights, demonstrating the versatility and potential of optimization techniques to drive innovation in systems engineering. Furthermore, it offers recommendations for future research directions and practical applications, emphasizing the importance of interdisciplinary approaches, algorithm development, and the adoption of advanced techniques in industry practices. This article aims to inform researchers and practitioners alike, fostering the continued evolution and application of optimization techniques in systems engineering.
 Keywords: Optimization Techniques, Systems Engineering, Swarm Intelligence, Machine Learning, Algorithm Development.
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