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@article{192336,
        author = {Mohd. Ameen Mobin Ansari and Yash Shyamrao Thakur},
        title = {PATHFINDING ALGORITHM VISUALIZER},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {9},
        pages = {1264-1270},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=192336},
        abstract = {Pathfinding algorithms play a fundamental role in enabling autonomous systems, games, and robotics to navigate efficiently through complex environments. Techniques such as Dijkstra’s Algorithm, A* Search, Greedy Best-First Search, and Breadth-First Search provide systematic methods for exploring state spaces and determining the optimal or near-optimal path between two points. A pathfinding visualizer serves as an interactive tool that illustrates how these algorithms operate in real time, revealing their decision-making processes, traversal patterns, and performance differences. By graphically representing node exploration, cost evaluation, and final path construction, visualizers support deeper conceptual understanding and aid in debugging, education, and algorithm comparison. Together, pathfinding algorithms and their visualizers form a powerful combination for analyzing navigation strategies and demonstrating computational problem- solving in an accessible and intuitive manner.
Pathfinding algorithms are essential computational techniques used to determine efficient routes within structured or unstructured environments. They form the backbone of numerous applications, including robotics, video games, geographic information systems, and network routing. Classical algorithms such as Breadth- First Search (BFS), Depth-First Search (DFS), Dijkstra’s Algorithm, Greedy Best- First Search, and A* Search each employ distinct strategies for exploring search spaces and evaluating movement costs. While some prioritize optimality and guarantee the shortest path, others emphasize speed and heuristic guidance to reduce computational overhead.},
        keywords = {},
        month = {February},
        }