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license: mit |
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Here's a comprehensive Hugging Face model card for your Particle Swarm Optimization PyQt5 application: |
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```markdown |
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--- |
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language: |
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- en |
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tags: |
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- optimization |
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- particle-swarm |
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- pso |
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- mathematical-optimization |
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- benchmark-functions |
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- pyqt5 |
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- gui |
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- visualization |
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- metaheuristics |
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- evolutionary-computation |
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library_name: pyqt5 |
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pipeline_tag: visualization |
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--- |
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# Particle Swarm Optimization Visualizer |
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## Model Overview |
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A comprehensive PyQt5 application that implements Particle Swarm Optimization (PSO) to solve 20 different mathematical optimization problems with real-time 2D and 3D visualizations. Watch particles oscillate and converge towards optimal solutions across various benchmark functions. |
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## Features |
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### 🎯 20 Optimization Problems |
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- **10 2D Benchmark Functions**: Sphere, Rosenbrock, Rastrigin, Ackley, Matyas, Himmelblau, Three-Hump Camel, Easom, Cross-in-Tray, Holder Table |
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- **10 3D Benchmark Functions**: Sphere 3D, Rosenbrock 3D, Rastrigin 3D, Ackley 3D, Sum of Different Powers, Rotated Hyper-Ellipsoid, Zakharov 3D, Dixon-Price, Levy 3D, Michalewicz 3D |
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### 📊 Real-time Visualizations |
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- **2D Contour Plots**: Particle movement over function landscapes |
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- **3D Surface Plots**: Interactive 3D optimization landscapes |
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- **Live Particle Tracking**: Watch particles oscillate and converge |
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- **Progress Monitoring**: Real-time optimization progress |
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### ⚙️ Customizable PSO Parameters |
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- Particle count (10-100) |
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- Iterations (10-500) |
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- Inertia weight (0.1-1.0) |
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- Cognitive parameter (0.1-2.0) |
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- Social parameter (0.1-2.0) |
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## Quick Start |
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### Installation |
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```bash |
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# Clone repository |
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git clone https://huggingface.co/TroglodyteDerivations/pso-pyqt5-visualizer |
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cd pso-pyqt5-visualizer |
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# Install dependencies |
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pip install -r requirements.txt |
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# Run the application |
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python app.py |
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``` |
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### Requirements |
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```txt |
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numpy>=1.21.0 |
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matplotlib>=3.5.0 |
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PyQt5>=5.15.0 |
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``` |
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## Usage |
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1. **Select Equation**: Choose from 20 benchmark functions |
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2. **Configure Parameters**: Adjust PSO parameters as needed |
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3. **Run Optimization**: Click "Run PSO" to start |
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4. **Visualize**: Watch real-time particle movement |
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5. **Analyze**: Review optimization results |
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## Application Interface |
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### Control Panel |
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- Equation selection with detailed descriptions |
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- PSO parameter configuration |
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- Interactive controls (Run, Pause, Step, Reset) |
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- Real-time progress tracking |
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- Results display |
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### Visualization Panel |
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- **Top**: 2D contour plots with particle trajectories |
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- **Bottom**: 3D surface plots showing optimization landscape |
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- Real-time updates during optimization |
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## Benchmark Functions |
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### 2D Functions |
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| Function | Description | Global Minimum | |
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|----------|-------------|----------------| |
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| Sphere | f(x,y) = x² + y² | (0,0) | |
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| Rosenbrock | f(x,y) = 100(y-x²)² + (1-x)² | (1,1) | |
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| Rastrigin | Multi-modal function | (0,0) | |
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| Ackley | Many local minima | (0,0) | |
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| Himmelblau | Four equal minima | Multiple | |
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### 3D Functions |
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| Function | Dimensions | Complexity | |
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|----------|------------|------------| |
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| Sphere 3D | 3 | Unimodal | |
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| Rastrigin 3D | 3 | Multi-modal | |
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| Michalewicz | 3 | Many local minima | |
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| Levy 3D | 3 | Complex landscape | |
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## PSO Algorithm |
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### Mathematical Formulation |
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Particle velocity and position updates: |
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``` |
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v_i(t+1) = w * v_i(t) + c1 * r1 * (pbest_i - x_i(t)) + c2 * r2 * (gbest - x_i(t)) |
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x_i(t+1) = x_i(t) + v_i(t+1) |
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``` |
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Where: |
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- `w`: Inertia weight |
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- `c1`, `c2`: Cognitive and social parameters |
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- `r1`, `r2`: Random numbers |
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- `pbest_i`: Particle's best position |
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- `gbest`: Global best position |
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### Key Features |
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- **Boundary Handling**: Particles bounce off boundaries |
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- **Velocity Clamping**: Prevents explosion |
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- **History Tracking**: Complete optimization history |
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- **Convergence Monitoring**: Real-time best value tracking |
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## Educational Value |
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This application serves as an excellent educational tool for: |
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- Understanding PSO algorithm behavior |
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- Visualizing optimization landscapes |
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- Comparing benchmark function characteristics |
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- Studying metaheuristic optimization |
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- Learning about multi-modal optimization |
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## Performance |
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### Optimization Capabilities |
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- **Convergence**: Rapid convergence on unimodal functions |
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- **Exploration**: Effective global search on multi-modal functions |
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- **Stability**: Robust performance across different landscapes |
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- **Scalability**: Handles 2D and 3D problems efficiently |
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### Visualization Performance |
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- **Smooth Animation**: 30+ FPS particle movement |
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- **Interactive Plots**: Zoom, pan, and rotate 3D views |
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- **Real-time Updates**: Instant parameter feedback |
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- **Memory Efficient**: Optimized for long runs |
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## Use Cases |
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### 🎓 Education |
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- Optimization algorithm courses |
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- Metaheuristic visualization |
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- Mathematical modeling classes |
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### 🔬 Research |
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- Algorithm benchmarking |
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- Parameter sensitivity analysis |
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- Optimization landscape study |
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### 💼 Industry |
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- Engineering optimization problems |
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- Machine learning hyperparameter tuning |
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- Financial modeling optimization |
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## Contributing |
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We welcome contributions! Areas for improvement: |
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- Additional benchmark functions |
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- Advanced PSO variants |
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- Export functionality |
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- Performance optimizations |
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- Additional visualization types |
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## Citation |
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If you use this application in your research or teaching, please cite: |
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```bibtex |
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@software{pso_pyqt5_visualizer, |
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title = {Particle Swarm Optimization PyQt5 Visualizer}, |
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author = {Martin Rivera}, |
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year = {2025}, |
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url = {https://huggingface.co/TroglodyteDerivations/pso-pyqt5-visualizer} |
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} |
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``` |
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## License |
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This project is licensed under the MIT License - see the LICENSE file for details. |
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## Support |
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For issues and questions: |
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- Open an issue on Hugging Face |
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- Check the documentation |
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- Review example configurations |
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## Model Card Authors |
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[TroglodyteDerivations] |
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## Model Card Contact |
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[https://huggingface.co/TroglodyteDerivations/Particle_Swarm_Optimization_Visualizer_PyQt5/edit/main/README.md] |
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--- |
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<div align="center"> |
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**✨ Watch particles find optimal solutions in beautiful visualizations! ✨** |
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</div> |
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``` |
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## Additional Files for Hugging Face |
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You should also create these files for your Hugging Face repository: |
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### `README.md` (same as above) |
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### `requirements.txt` |
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```txt |
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numpy>=1.21.0 |
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matplotlib>=3.5.0 |
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PyQt5>=5.15.0 |
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``` |
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### `app.py` |
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### `LICENSE` |
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```txt |
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MIT License |
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Copyright (c) 2025 [Martin Rivera] |
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Permission is hereby granted, free of charge, to any person obtaining a copy |
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of this software and associated documentation files (the "Software"), to deal |
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in the Software without restriction, including without limitation the rights |
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell |
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copies of the Software, and to permit persons to whom the Software is |
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furnished to do so, subject to the following conditions: |
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The above copyright notice and this permission notice shall be included in all |
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copies or substantial portions of the Software. |
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR |
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, |
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE |
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER |
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, |
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE |
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SOFTWARE. |
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``` |
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### `.gitattributes` |
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```gitattributes |
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*.py filter=lfs diff=lfs merge=lfs -text |
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*.png filter=lfs diff=lfs merge=lfs -text |
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*.jpg filter=lfs diff=lfs merge=lfs -text |
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``` |
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This model card provides comprehensive documentation for your PSO PyQt5 application and makes it ready for sharing on Hugging Face Hub! |
