ALGORITHMS AND DATA STRUCTURES

Sorting algorithms are one of the most important topics in computer science and software engineering because they play a critical role in organizing and processing data efficiently. With the rapid growth of digital information, efficient sorting methods have become essential in databases, search engines, artificial intelligence systems, operating systems, and e-commerce platforms. This article examines the concept of algorithms and data structures with a special focus on sorting algorithms and their analytical evaluation.The paper discusses several fundamental sorting algorithms, including Bubble Sort, Selection Sort, Insertion Sort, Merge Sort, and Quick Sort. Each algorithm is analyzed based on its working mechanism, time complexity, memory usage, advantages, disadvantages, and practical applications. The study also explains the importance of Big O notation in evaluating algorithm efficiency and compares different algorithms using theoretical and practical perspectives. Furthermore, the article highlights how modern computing systems handle large-scale data sorting through parallel processing and external sorting techniques. The findings show that while simple algorithms are suitable for educational purposes and small datasets, advanced algorithms such as Merge Sort and Quick Sort provide significantly better performance for large-scale applications.Overall, the study demonstrates that understanding sorting algorithms and their complexities is essential for designing efficient software systems and improving computational performance in modern technology environments.排序算法是计算机科学与软件工程领域最为核心的研究主题之一，因其在高效组织与处理数据方面发挥着至关重要的作用。随着数字信息的爆发式增长，高效的排序方法已成为数据库、搜索引擎、人工智能系统、操作系统以及电子商务平台不可或缺的核心组件。本文围绕算法与数据结构的核心概念展开探讨，重点聚焦排序算法及其分析评测。本文讨论了多种基础排序算法，包括冒泡排序（Bubble Sort）、选择排序（Selection Sort）、插入排序（Insertion Sort）、归并排序（Merge Sort）与快速排序（Quick Sort）。本文将从运行原理、时间复杂度、内存占用、优劣特性以及实际应用场景等维度，对每种算法展开逐一分析。此外，本研究还阐释了大O标记法（Big O notation）在评测算法效率中的重要价值，并从理论与实践双维度对各类排序算法进行对比分析。此外，本文还探讨了现代计算系统如何通过并行处理与外部排序技术，实现大规模数据的排序处理。研究结果表明，尽管基础排序算法适用于教学场景与小规模数据集，但归并排序、快速排序等高级排序算法在大规模应用场景中可实现显著更优的性能表现。综上，本研究证实，深入理解排序算法及其复杂度特性，对于设计高效软件系统、提升现代技术环境下的计算性能具有至关重要的意义。