Abstract

Search algorithms form the backbone of computer science applications ranging from information retrieval and artificial intelligence to database management and network optimization. Although their theoretical complexities are well studied, practical performance can vary significantly depending on the choice of programming language and runtime environment. This study presents a comparative performance analysis of widely used search algorithms Linear Search, Binary Search, Depth-First Search (DFS), Breadth-First Search (BFS), and A Search* are implemented in two popular programming languages: Java and Python. The analysis focuses on measuring execution time across varying dataset sizes and graph structures to highlight differences in efficiency, scalability, and runtime behavior. Empirical results demonstrate that while Java generally outperforms Python in computation-intensive tasks due to its compiled nature and Just-In-Time (JIT) optimizations, exceptions arise in certain cases. The findings of this research emphasize that performance cannot be judged solely on algorithmic theory; instead, language characteristics, data structures, memory models, and runtime environments play crucial roles in determining practical efficiency. The study concludes with insights into the suitability of Java versus Python for algorithm-intensive applications, offering guidance for researchers, educators, and software developers in selecting the right combination of algorithm and language for performance-critical systems.

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