Java implementation of Dijkstra's Algorithm for pathfinding. It was developed during my school computer science lessons and got implemented in Greenfoot. It simulates a rover navigating a grid using node and edge classes.
About SmartXNavigator is an advanced route planning tool that leverages geographic and traffic data to provide optimized travel routes. Utilizing Pathfinding algorithms, it calculates the shortest or quietest paths based on user preferences.
#1 try to implement graphs in Javascript (in HTML files)
visualizes five pathfinding algorithms and maze generation algorithms.
Java app to visualize swiss trains network with Dijkstra and Floyd
A Java-based public transport routing engine for Maastricht, integrating GTFS data, shortest-path algorithms, and urban accessibility metrics via a Java Swing GUI.
React-based web application to visualize Path Finding algorithms like Dijkstra, BFS, and DFS.
Hot Hẻm: Routing tool not for the fastest nor the coolest path, but the hottest path. A downscaled case study in Sài Gòn, Việt Nam (Hồ Chí Minh City / HCMC) districts 1, 2, and 8.
Visualizer for some search algorithms — currently A* and Dijkstra's algorithms — using JavaScript, jQuery, HTML, CSS, and some third party JS libraries.
Dynamic Routing (Single-Source Shortest-Path Algorithms) with Network Graph Editor GUI
A WebApp was created to solve for a case study on finding the shortest paths to deliver products and linearly assigning problem in a warehouse. The app is based on the Branch and Bound algorithm for the TSP and the Hungarian algorithm for the VAS assignment.
Shortest path between cities in Colombia
Javascript Dijstras shortest path calculator in a Directed Weighted Acyclic graph
The projects models the flightpath of a drone while it makes deliveries. The projects compares different algorithms and their suitability.
It's a simple yet powerful pathfinding application that utilizes Dijkstra's algorithm to find the shortest path between multiple nodes in a graph.
nextdataAI: a Python library for solving mazes using classical algorithms (A*, BFS, DFS, Dijkstra, Greedy), reinforcement learning (QLearning, QTable, QNN, QLSTM), and novel pseudo-heuristics. Features include a neural network-based heuristic approximating Manhattan distance (NNHeuristic) and a custom SManhattan heuristic, a dataset gener
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