aura-path-algorithms

AURA - Advanced Unified Routing Algorithms: A modern ES package providing graph shortest-path algorithms (Dijkstra, Bellman-Ford, Floyd-Warshall) with benchmarks and tests.

Elegant AI-driven routing with balance, performance, and adaptability.

🌌 Introduction

AURA (Adaptive Unified Routing Algorithm) is a next-generation algorithmic framework designed to optimize routing, balancing, and resource distribution in dynamic systems.
It redefines efficiency by combining adaptive learning, statistical weight redistribution, and benchmark-driven evaluation into a unified ecosystem.

Born from the evolution of the Weighted Balance Redistribution (WBR) concept, AURA introduces a more modular, scalable, and AI-aligned approach that is intuitive, powerful, and future-proof.

🚀 Core Features

• 🔄 Adaptive Unified Routing
  Dynamically balances load, routes intelligently, and adapts to real-time changes.

• 📊 ES Package with Benchmarking
  Includes the Evaluation & Simulation (ES) module for performance benchmarking, enabling precise testing across different workloads.

• ⚖️ Weighted Balance Redistribution (Legacy)
  Retains the original WBR logic as a compatibility layer, ensuring backward support.

• ⚡ High Performance
  Built with efficient computational techniques to ensure scalability across diverse systems.

• 🧩 Modular Architecture
  Plug-and-play design makes AURA suitable for networking, distributed systems, AI routing, logistics optimization, and more.

📦 Installation

Clone the repository:

git clone https://github.com/Dr-Nio/aura-path-algorithms.git
cd aura

Install dependencies:
npm install

Or, if using yarn:
yarn install

🛠 Usage:

Run the AURA Core
node aura-core.js

Run ES Benchmarking
node es-benchmark.js

Example usage:

import { dijkstra, aura } from "@dr-nio/aura-path-algorithms";

const graph = { A: { B: 1 }, B: { C: 2 }, A: { C: 4 } };
console.log(dijkstra(graph, "A").distances);
Features (why Aura is unique).

Link to tests/examples.



Example Output
[AURA] Adaptive Unified Routing initialized.
[ES] Running benchmark on 10,000 simulated requests...
[RESULT] Efficiency: 94.8% | Latency Reduction: 21% | Balance Stability: Optimal         # This file

📂 Project Structure**

aura-path-algorithms/
 ├── src/                 # Source code for the AURA adaptive routing algorithm
 ├── tests/               # Unit and integration tests (Vitest)
 ├── docs/                # Documentation and guides
 ├── LICENSE.md           # AURA License
 └── README.md            # Project overview and usage instructions

🤝 Contributing

We welcome contributions to AURA! To get started:

Fork this repository

Create a feature branch (git checkout -b feature/amazing-feature)

Commit your changes (git commit -m 'Add amazing feature')

Push to the branch (git push origin feature/amazing-feature)

Open a Pull Request

📜 License

This project is licensed under the AURA License (see LICENSE.md).

🌟 Why AURA?

Unlike traditional algorithms, AURA feels alive:

Adaptive – learns and adjusts dynamically

Unified – integrates routing, balancing, and benchmarking seamlessly

Resilient – optimized for edge cases and dynamic scaling

Aesthetic – designed with clarity and modern engineering best practices

AURA is more than an algorithm—it’s a framework for intelligent balance.

⚖️ Legal Safety Note

This project includes implementations of classical graph algorithms such as Dijkstra, Bellman–Ford, and Floyd–Warshall.
These algorithms are named after the researchers who first described them in academic publications. The algorithms themselves are public domain concepts and cannot be copyrighted or patented.

• ✅ Algorithm names are widely used in textbooks, research, and open-source projects.
• ✅ No copyright, patent, or trademark restrictions apply to their usage.
• ✅ You are free to use these implementations in open-source or commercial projects under the terms of this repository's license.