Optimized Algorithms and Their Practical Applications in Real-World Scenarios

Abstract

Optimized algorithms are fundamental to improving the efficiency of computational systems in various real-world applications. As the demand for faster, more scalable, and resource-efficient solutions continues to rise, the role of optimized algorithms becomes increasingly significant. These algorithms are designed to minimize the use of computational resources, including time and memory, while still delivering accurate results. This article reviews various types of optimized algorithms, such as divide-and-conquer, greedy, dynamic programming, and machine learning algorithms, and highlights their practical applications across diverse sectors, including big data, cloud computing, artificial intelligence, e-commerce, healthcare, and cybersecurity. The review also addresses the challenges faced in developing these algorithms, such as scalability and real-time processing, and explores the future directions for algorithm optimization in the context of emerging technologies like quantum computing and machine learning-driven optimization.