Sorting Algorithm Visualizer & Performance Analyzer

Authors

  • Rohan Ankush Kakade Department of Computer Applications, GH Raisoni College of Engineering and Management, Jalgaon, India
  • Aastha Dattatray Akotkar Department of Computer Applications, GH Raisoni College of Engineering and Management, Jalgaon, India
  • Rutuja Vinod Narote Department of Computer Applications, GH Raisoni College of Engineering and Management, Jalgaon, India
  • Ritesh Dattu Koli Department of Computer Applications, GH Raisoni College of Engineering and Management, Jalgaon, India

Keywords:

Sorting Algorithms, Algorithm Visualization, Performance Analysis, Bubble Sort, Insertion Sort, Merge Sort, Data Structures and Algorithms (DSA), Sorting Visualization, Algorithm Complexity, Execution Time, Comparisons, Swaps, Interactive Learning, Web-Based Application, Algorithm Efficiency.

Abstract

The Sorting Algorithm Visualizer & Performance Analyzer is an interactive web-based application developed to simplify the understanding of sorting algorithms through visual representation and performance analysis. Sorting algorithms are an essential part of Data Structures and Algorithms (DSA), but understanding their internal operations can be difficult through theoretical explanations alone.

The proposed system visually demonstrates algorithms such as Bubble Sort, Insertion Sort, and Merge Sort using animated bars. It allows users to generate or enter data and observe operations such as comparisons, swaps, and movements in real time. Along with visualization, the system analyzes algorithm performance using parameters such as execution time, number of comparisons, and number of swaps.

The application helps users compare different sorting techniques and understand their time complexity and efficiency for different datasets. It provides a simple and interactive learning environment for students, making DSA concepts more practical, engaging, and easier to understand. The project can be further enhanced by adding advanced sorting algorithms, performance graphs, AI-based recommendations, and additional learning features.

References

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Published

2026-09-29