Personal Information:
- Name: Abhishek Kumar Verma
- GitHub Repo: Detailed_PathFinding_Visualizer
Pathfinding Visualizer:
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Introduction: Welcome to Pathfinding Visualizer! This application was built by me to visualize various pathfinding algorithms in action. The primary goal was to create a tool for users to explore and understand the workings of these algorithms through interactive visualization.
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Access Link: You can access the Pathfinding Visualizer using this link: Pathfinding Visualizer (please use Google Chrome for the best experience).
Supported Algorithms: The Pathfinding Visualizer supports the following algorithms:
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Dijkstra's Algorithm (weighted): Often considered the father of pathfinding algorithms, Dijkstra's guarantees the shortest path between nodes.
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A Search* (weighted): Arguably the best pathfinding algorithm, A* Search uses heuristics to find the shortest path faster than Dijkstra's Algorithm.
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Greedy Best-first Search (weighted): A faster and more heuristic-heavy version of A* that does not guarantee the shortest path.
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Swarm Algorithm (weighted): A swarm algorithm is a type of optimization algorithm inspired by the collective behavior of social swarms or groups of living organisms, such as ants, bees, birds, or fish. These algorithms are used to solve complex problems by simulating the interactions between individual agents within a population, where each agent makes decisions based on its local knowledge and the influence of neighboring agents. The collective intelligence of the swarm emerges from the interactions of these individual agents.
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Convergent Swarm Algorithm (weighted): A faster, more heuristic-heavy version of the Swarm Algorithm that also does not guarantee the shortest path.
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Bidirectional Swarm Algorithm (weighted): A variation of the Swarm Algorithm that explores from both the start and target nodes, but does not guarantee the shortest path.
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Breath-first Search (unweighted): A great algorithm that guarantees the shortest path.
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Depth-first Search (unweighted): A less optimal algorithm for pathfinding that does not guarantee the shortest path.
Maze Generation Algorithm: Apart from pathfinding algorithms, I implemented a Recursive Division Maze Generation algorithm.
About the Swarm Algorithm: A swarm algorithm is a type of optimization algorithm inspired by the collective behavior of social swarms or groups of living organisms, such as ants, bees, birds, or fish. These algorithms are used to solve complex problems by simulating the interactions between individual agents within a population, where each agent makes decisions based on its local knowledge and the influence of neighboring agents. The collective intelligence of the swarm emerges from the interactions of these individual agents.
One of the most popular swarm algorithms is the Particle Swarm Optimization (PSO) algorithm, which was originally developed by Dr. Eberhart and Dr. Kennedy in 1995. In PSO, each agent (particle) in the swarm represents a potential solution to the problem being optimized. The algorithm starts by randomly placing particles in the search space and then iteratively updates their positions and velocities based on their historical best positions and the best positions found by their neighbors.
The basic steps of the PSO algorithm are as follows:
- Initialization: Initialize a population of particles with random positions and velocities in the search space.
- Evaluation: Evaluate the fitness of each particle (solution) based on the problem's objective function.
- Update Particle's Best Position: Update the best position (individual best) found by each particle so far.
- Update Swarm's Best Position: Determine the global best position (swarm best) among all the particles' best positions.
- Update Velocities and Positions: Update each particle's velocity and position based on its own best position and the swarm's best position.
- Repeat: Iterate the process until a termination criterion is met (e.g., a maximum number of iterations or a satisfactory solution is found).
The PSO algorithm effectively explores the search space by balancing exploration and exploitation, helping to find optimal or near-optimal solutions in complex, high-dimensional spaces.
Other examples of swarm algorithms include Ant Colony Optimization (ACO), which is inspired by the foraging behavior of ants, and the Firefly Algorithm, which simulates the flashing patterns of fireflies to find optimal solutions.
Swarm algorithms are particularly useful for solving optimization problems in various domains, including engineering, logistics, telecommunications, and artificial intelligence. Their parallel nature and ability to escape local optima make them powerful tools for finding global solutions efficiently.