Harmonic Swarm Intelligence (HSI)

Overview

Harmonic Swarm Intelligence (HSI) is a novel AI framework that integrates wave-based computation, swarm intelligence, and dynamic multi-sensory perception to explore and interpret hyperdimensional structures. Inspired by echolocation, harmonic resonance, and emergent intelligence, HSI enables autonomous agent swarms to collaboratively map and interact with complex environments in ways traditional AI cannot.

Key Features

• Multi-Sensory Swarm Agents: Each agent possesses unique sensory capabilities, including frequency analysis, wave interference detection, topological distortion sensing, and geometric measurement.
• Acoustic Intelligence: Unlike conventional AI, HSI perceives its environment via wave-based exploration, utilizing resonance mechanics to interpret complex spaces.
• Swarm Queen Optimization: A central decision-making entity analyzes agent feedback, refines perception, and directs swarm behavior for optimal convergence.
• Meta-Learning & Adaptation: The system dynamically adjusts agent weights, prioritizing high-confidence sensory inputs and recalibrating underperforming agents.
• Hyperdimensional Exploration: Designed for 4D+ problem spaces, HSI excels in understanding structures that defy Euclidean intuition.
• Scalable & Open-Source: Built with Python, NumPy, Matplotlib, and SciPy, making it extensible for research and practical applications.

Installation

To use Harmonic Swarm Intelligence, clone the repository and install the dependencies:

git clone https://github.com/richardaragon/harmonicswarmintelligence.git
cd harmonic-swarm-intelligence
pip install -r requirements.txt

Usage

Run a simulation using the default hyperdimensional structure and multi-agent swarm:

python run_simulation.py

Configuration

You can modify simulation parameters in config.py:

NUM_AGENTS = 16  # Number of agents in the swarm
DIMENSION = 4    # Dimensionality of the explored space
STEPS = 100      # Number of simulation steps
VISUALIZE_EVERY = 20  # Frequency of visualization updates

How It Works

1. Agents Probe the Environment: Each agent emits wave-like signals and records responses based on its sensory type.
2. Swarm Queen Processes Feedback: The Queen evaluates agent confidence, adjusts weights, and refines the global model.
3. Exploration & Adaptation: Agents move dynamically, seeking optimal resonance interactions with the structure.
4. Convergence & Mapping: Over time, the system constructs an accurate model of the hyperdimensional shape.

Visualizing Results

HSI includes built-in 3D and 2D visualizations to track agent movements, convergence metrics, and knowledge accumulation.

from hsi.visualization import plot_results
plot_results('simulation_output.json')

Applications

• AI Geometry & Hyperdimensional Computing
• Swarm Robotics & Collective Intelligence
• Acoustic AI & Wave-Based Computation
• Graph Theory & Spectral Analysis
• Optimization in High-Dimensional Spaces

Contributing

We welcome contributions! To contribute:

1. Fork the repository.
2. Create a new branch (git checkout -b feature-branch).
3. Commit your changes (git commit -m 'Add new feature').
4. Push to your branch (git push origin feature-branch).
5. Open a Pull Request.

License

Harmonic Swarm Intelligence (HSI) is released under the MIT License. See LICENSE for details.

Acknowledgments

Special thanks to the research community exploring AI Geometry, resonance-based intelligence, and swarm learning. Your work continues to inspire new frontiers in AI and computational intelligence.