PPT.pptx
- 2. 01 INTRODUCTION TABLE OF CONTENTS 03 IMPLEMENTATION 04 METHODOLOGY 05 TECHNICAL DISCUSSION 07 CONCLUSION 06 DEMONSTRATION 02 PROBLEM STATEMENT
- 3. INTRODUCTION 01 • This project allows users to understand and explore the inner workings of various pathfinding algorithms in a visual and interactive manner. It provides a dynamic representation of the algorithms, enabling users to observe how different techniques navigate through a graph or network to find the optimal path. • The visualizer allows users to experiment with different scenarios and parameters. They can add the constraints or Randomize it with the shortest path and observe how these modifications affect the algorithm's behavior and the resulting path. This interactive aspect promotes learning and empowers users to gain insights into algorithm performance and optimization. • It promotes understanding, experimentation, and optimization, making it an invaluable tool for both learning and practical applications in fields where efficient pathfinding is crucial.
- 4. PROBLEM STATEMENT 02 • The problem addressed by the project is the difficulty in comprehending the inner workings of pathfinding algorithms without visual representation. Textual explanations alone may not effectively convey the complexities of the pathfinding process, making it challenging for users to evaluate the algorithms’ efficiency and effectiveness. • The lack of a user-friendly platform that offers interactive visualizations hinders users’ ability to grasp the performance of different algorithms in diverse scenarios.
- 5. METHODOLOGY 03 • UI libraries that provide interactive graphics capabilities suitable for your pathfinding algorithm visualizer. Some popular choices include ReactJS with libraries • libraries that offer features like drawing nodes and edges, handling user interactions, and updating the visualization in real-time. • Implementation of the selected pathfinding algorithms, such as Dijkstra's algorithm, A* algorithm, or Breadth-first search (BFS), according to the chosen UI library's conventions. • Ensure the algorithms correctly navigate the graph, update the visualization in each step, and track visited nodes and the final path. CHOOSING FRAMEWORK GRAPH DATA STRUCTURE IMPLEMENTATION
- 6. TECHNICAL DISCUSION 03 • Depth-First Search (DFS) is a graph traversal algorithm that explores deeply along each branch before backtracking. It starts from a given node, explores as far as possible, and backtracks to visit other unvisited nodes. • DFS can be implemented using a stack or recursion and is commonly used for traversing or searching connected components in a graph. • While DFS may not guarantee the shortest path in pathfinding, it is useful for scenarios like maze- solving or exhaustive graph exploration. • Breadth-First Search (BFS) is a widely used graph traversal algorithm that explores vertices in breadth-first order. • It starts from a given node and visits all its neighboring nodes before moving on to their neighbors, and so on. • BFS can be implemented using a queue or a deque and is commonly used to find the shortest path in an unweighted graph. • In pathfinding, BFS guarantees to find the shortest path in terms of the number of edges, making it suitable for scenarios where equal-cost paths are desired or when weights are not present.
- 7. • Dijkstra's Algorithm is an efficient algorithm for finding the shortest path between nodes in a weighted graph. • It starts from a source node and expands the frontier by visiting neighboring nodes with the lowest cumulative cost. • By using a priority queue or min-heap, it efficiently selects the node with the minimum cost at each step. Dijkstra's Algorithm maintains a distance table to track the shortest distances and a previous node table to reconstruct the shortest path. • It finds application in navigation systems, routing protocols, and network optimization. • The A* (A-star) algorithm is an informed search algorithm used for pathfinding. It combines BFS and Dijkstra's Algorithm by considering both the cost to reach a node and an estimated cost to the goal. • A* uses a heuristic function like Manhattan or Euclidean distance to estimate remaining costs. • It maintains a priority queue based on combined costs and heuristic values to guide the search. • A* is widely used in GPS navigation, video games, and robot motion planning.
- 8. React is a JavaScript library for building user interfaces, known for its component-based architecture and efficient rendering. When creating a path visualizer using React, we will utilize its core features to develop a dynamic and interactive user interface. React allows to break down of the visualizer into reusable components like Graph, Node, Edge, Controls, and Visualization Area, enabling modular development and code reusability. With React's virtual DOM and efficient diffing algorithm, we can update only the necessary components, ensuring the smooth and efficient rendering of the visualization as the pathfinding algorithm progresses. React's state management capabilities facilitate the tracking of algorithm progress, enabling real-time updates to the visualization. By leveraging React's strengths, We can create a powerful and visually appealing path visualizer that provides an engaging and intuitive experience for users to explore and understand pathfinding algorithms. React
- 9. IMPLEMENTATION 05 • Creating a data structure to represent the graph. This could involve storing node coordinates, adjacency lists or matrices, and weights/costs for edges. • Implementation of the pathfinding algorithms to visualize, such as Dijkstra's algorithm, A* algorithm, or BFS, in separate algorithm files. • Writing the functions to execute the chosen algorithms and updating the graph data structure and visualization accordingly. • Update the graph data structure and trigger the appropriate algorithm functions based on user input. • Use CSS or inline styles to animate or highlight the algorithm's execution, such as marking visited nodes or animating the pathfinding process. • Update the Graph and Node components to visually represent the graph, including nodes, edges, and any obstacles. • Use React's lifecycle methods or useEffect hook to update the visualization in real-time as the algorithm progresses.
- 10. • Implementation of event handlers for user interactions, such as randomizing selecting start and end points with adding random obstacles, or adjusting visualization settings. • Update the visualization and trigger the appropriate algorithms or graph operations based on user input. • Utilize the capabilities of the UI library to update the visualization in real time. Use animations or transitions to show the algorithm's progress and visualize the pathfinding process step by step. USER HANDLE INTERACTION USER INTERFACE
- 11. DEMONSTRATION 06
- 12. CONCLUSION 07 • In conclusion, pathfinding algorithm visualizers created using React offer an interactive way to understand and explore algorithms like DFS, BFS, Dijkstra's Algorithm, and A*. These visualizers leverage React's capabilities to provide a seamless and efficient platform for building engaging visualizations. • Users can gain insights into the algorithms' behavior and applications, enhancing their problem-solving skills and fostering a passion for algorithmic exploration. • The combination of React and pathfinding algorithm visualizers not only fosters a deeper understanding of these algorithms but also enhances problem-solving skills and fosters a passion for algorithmic exploration.
- 13. THANK YOU