Advancing Solutions with Nature-Inspired Algorithms
ARIY is an interactive visualization tool that demonstrates how Ant Colony Optimization (ACO) can solve the Travelling Salesman Problem (TSP). It bridges the gap between theory and practice by allowing learners, educators, and researchers to experiment with parameters, observe ant traversal, and understand how nature-inspired intelligence solves complex optimization challenges.
Traditional learning of metaheuristic algorithms is often abstract and difficult to visualize.
| Challenge | Description |
|---|---|
| Understanding Metaheuristic Algorithms | Algorithms like ACO are math-heavy and difficult to grasp. |
| Lack of Interactive Tools | Most resources provide static or purely mathematical implementations. |
| Connecting Theory to Real-World | Learners struggle to apply optimization theory to logistics, AI, and networking. |
| Experimenting with Parameters | Effects of α, β, ρ, and ant count are non-intuitive. |
👉 ARIY solves this by making ACO visual, interactive, and learner-friendly.
Vision: To foster a deeper understanding of intelligent systems by showing how nature-inspired algorithms like ACO solve real-world challenges.
Mission:
- Simplify Complex Algorithms
- Bridge Learning & Application
- Empower Educators & Students
- Promote Nature-Inspired Thinking
- Enable Real-Time Parameter Exploration
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Travelling Salesman Problem (TSP): Find the shortest route that visits all nodes exactly once.
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Ant Colony Optimization: Ants explore paths using pheromones + distance knowledge.
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Probability Rule:
- Ant chooses the next city based on pheromone level (α) and heuristic knowledge (β).
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Pheromone Update Rule:
- Pheromones evaporate (ρ) and are reinforced on shorter paths.
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Iterations: After multiple rounds, near-optimal paths emerge.
You can tweak parameters live:
- 🐜 Ant population
- 💨 Evaporation rate (ρ)
- 🧭 Pheromone influence (α)
- 📏 Distance heuristic influence (β)
- Interactive Visualization of ants solving TSP.
- Real-time Parameter Tuning (α, β, ρ, ant count).
- Path Updates as simulation progresses.
- Live Reset when parameters are changed.
- Modern UI built with React, TailwindCSS, Flowbite & Vite.
- React.js
- Vite.js
- Tailwind CSS
- Flowbite + Hyper UI
This project is licensed under the MIT License – see LICENSE for details.
Kshitij K Sawant Software Developer | AI & Data Science Enthusiast | Educator