README.md

Pathfinding Algorithms: Dijkstra's and A*

Overview

This project explores two prominent algorithms used for pathfinding in computational tasks: Dijkstra's Algorithm and A*. Both algorithms are foundational for solving shortest-path problems, with distinct strengths and ideal use cases.

• Dijkstra's Algorithm: Introduced in 1956, it systematically explores all possible routes to guarantee the shortest path.
• A_Star (A*) Algorithm: Developed in 1968, it enhances Dijkstra's method by incorporating heuristics to prioritize promising paths, improving efficiency.

Algorithms Overview

Dijkstra's Algorithm

Dijkstra's algorithm is a deterministic method designed to find the shortest path in a graph. It is particularly effective for applications where accuracy is critical.
Key Features:

• Guarantees the shortest path by systematically exploring all routes.
• Ideal for applications such as network routing, transportation systems, and optimization problems.
• Does not use heuristics, which may lead to slower performance when exploring large graphs or sparse connections.

Pseudocode:

function Dijkstra(graph, source, target)
    dist = Dictionary()  # Distance from source to each node
    prev = Dictionary()  # Previous node in optimal path
    for each node in graph
        dist[node] = ∞
        prev[node] = null
    dist[source] = 0

    Q = PriorityQueue()  # Min-heap priority queue
    Q.push(source, 0)

    while Q is not empty
        current = Q.pop()  # Node with smallest distance

        if current == target
            break

        for neighbor in graph.neighbors(current)
            alt = dist[current] + graph.weight(current, neighbor)
            if alt < dist[neighbor]
                dist[neighbor] = alt
                prev[neighbor] = current
                Q.push(neighbor, alt)

    return reconstructPath(prev, source, target)

function reconstructPath(prev, source, target)
    path = []
    current = target
    while current is not null
        path.prepend(current)
        current = prev[current]
    return path

A* Algorithm

A* algorithm builds on Dijkstra’s principles by integrating heuristics to optimize the search process. It is particularly suited for dynamic and time-sensitive applications.
Key Features:

• Combines the actual cost of reaching a node with an estimated cost to the goal.
• Uses heuristics to guide the search, significantly reducing the computational load.
• Commonly used in game development, robotics, and real-time navigation systems.

Pseudocode:

function AStar(graph, source, target, heuristic)
    dist = Dictionary()  # Distance from source to each node
    prev = Dictionary()  # Previous node in optimal path
    for each node in graph
        dist[node] = ∞
        prev[node] = null
    dist[source] = 0

    Q = PriorityQueue()  # Min-heap priority queue
    Q.push(source, heuristic(source, target))

    while Q is not empty
        current = Q.pop()  # Node with smallest f(n) = g(n) + h(n)

        if current == target
            break

        for neighbor in graph.neighbors(current)
            tentative_g = dist[current] + graph.weight(current, neighbor)
            if tentative_g < dist[neighbor]
                dist[neighbor] = tentative_g
                prev[neighbor] = current
                f_score = tentative_g + heuristic(neighbor, target)
                Q.push(neighbor, f_score)

    return reconstructPath(prev, source, target)

Comparison

Feature	Dijkstra's Algorithm	A* Algorithm
Optimality	Always guarantees the shortest path.	Depends on the heuristic (admissible/consistent).
Efficiency	Explores all possible paths.	Focuses on the most promising paths.
Use Cases	Network routing, large graphs.	Real-time systems, dynamic environments.
Heuristics	Not used.	Essential for guiding the search.

Trade-offs

• Dijkstra’s Algorithm: Precision at the cost of efficiency.
• A Algorithm*: Speed at the risk of suboptimality if the heuristic is poorly designed.

Practical Applications

• Dijkstra's Algorithm: Suitable for large-scale network routing problems where precision is more important than speed.
• A* Algorithm: Ideal for time-sensitive scenarios like game development or autonomous vehicle navigation.

Key Insights

• Dijkstra’s algorithm employs an "uninformed search" approach, systematically evaluating all nodes.
• A* algorithm exemplifies "informed search", using heuristics to prioritize paths, enhancing performance in complex environments.

Project Structure

Files

• Comparison_Test.py: Python script to compare the performance of the two algorithms.
• implementation.py: Script that implements both algorithms and allows experimentation with different graph structures.
• Figure:
  • Figure_1-1.png: A visual representation of algorithm comparisons.

Getting Started

Requirements

• Python (with NetworkX for graph analysis)
• Any compatible IDE or text editor

Example Usage

Implementations of both algorithms can be found in the src directory. Run the scripts to visualize how each algorithm handles different graph structures and pathfinding scenarios.

Running the Code

1. Clone the repository:

    git clone https://github.com/leonking1990/CSCI-411-project_report.git

2. Navigate to the project folder

    cd pathfinding-algorithms

3. Install dependencies:

    pip install -r requirements.txt

4. Run the comparison test:

    python Comparison_Test.py

Visuals

Refer to the figure (Figure_1-1.png) for graphical insights into the algorithm's performance and heuristics.

References

• GeeksforGeeks. "Difference Between Dijkstra’s Algorithm and A* Search Algorithm." Read here.
• Stanford University. "A* Comparison." Read here.
• Coding Clutch. "The Most Popular Pathfinding Algorithms Explained: A* to Dijkstra’s." Read here.