Investigating Optimization Methods in Computer Science Engineering: A Comprehensive Study

Abstract

In this paper, we will examine numerous optimization approaches in the field of computer science engineering in depth, shedding light on their applications, strengths, and weaknesses. Optimization algorithms are important tools in computer science engineering, with applications spanning from machine learning to computer vision, data mining, robotics, and more. In principle, optimization algorithms strive to locate the best possible solution among a group of possibilities while taking certain objectives and restrictions into account. They are the foundation of problem-solving approaches, providing a systematic and efficient approach to dealing with multiple difficulties. The efficiency and efficacy of each algorithm vary from one another, and each algorithm has advantages and limits that rely on the applications they are used with. We intend to provide a comprehensive view of optimization algorithms. We will cover their many types, delving into their real-world applications and painstakingly analyzing their strengths and weaknesses. In addition, we will investigate the complexities of each algorithm, giving light on the specific characteristics and settings in which they shine. This work seeks to serve as a basic resource for computer science engineering academics and practitioners, developing a deeper understanding of optimization algorithms and stimulating more inquiry in this dynamic field.