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Particle_Swarm_Optimization_Visualizer_PyQt5

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Here's a comprehensive Hugging Face model card for your Particle Swarm Optimization PyQt5 application:

---
language:
- en
tags:
- optimization
- particle-swarm
- pso
- mathematical-optimization
- benchmark-functions
- pyqt5
- gui
- visualization
- metaheuristics
- evolutionary-computation
library_name: pyqt5
pipeline_tag: visualization
---

# Particle Swarm Optimization Visualizer

## Model Overview

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.

![Screenshot 2025-11-03 at 2.01.25 PM](https://cdn-uploads.huggingface.co/production/uploads/68401f649e3f451260c68974/rHMP2HOOh15LyGkVba9fs.png)
![Screenshot 2025-11-03 at 2.02.02 PM](https://cdn-uploads.huggingface.co/production/uploads/68401f649e3f451260c68974/Pxszkv7-fFKqGqfqp5ROG.png)

## Features

### 🎯 20 Optimization Problems
- **10 2D Benchmark Functions**: Sphere, Rosenbrock, Rastrigin, Ackley, Matyas, Himmelblau, Three-Hump Camel, Easom, Cross-in-Tray, Holder Table
- **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

### 📊 Real-time Visualizations
- **2D Contour Plots**: Particle movement over function landscapes
- **3D Surface Plots**: Interactive 3D optimization landscapes
- **Live Particle Tracking**: Watch particles oscillate and converge
- **Progress Monitoring**: Real-time optimization progress

### ⚙️ Customizable PSO Parameters
- Particle count (10-100)
- Iterations (10-500)
- Inertia weight (0.1-1.0)
- Cognitive parameter (0.1-2.0)
- Social parameter (0.1-2.0)

## Quick Start

### Installation

```bash
# Clone repository
git clone https://huggingface.co/TroglodyteDerivations/pso-pyqt5-visualizer
cd pso-pyqt5-visualizer

# Install dependencies
pip install -r requirements.txt

# Run the application
python app.py

Requirements 

numpy>=1.21.0
matplotlib>=3.5.0
PyQt5>=5.15.0

Usage

  1. Select Equation: Choose from 20 benchmark functions
  2. Configure Parameters: Adjust PSO parameters as needed
  3. Run Optimization: Click "Run PSO" to start
  4. Visualize: Watch real-time particle movement
  5. Analyze: Review optimization results

Application Interface

Screenshot 2025-11-03 at 2.02.38 PM Screenshot 2025-11-03 at 2.03.29 PM

Control Panel

  • Equation selection with detailed descriptions
  • PSO parameter configuration
  • Interactive controls (Run, Pause, Step, Reset)
  • Real-time progress tracking
  • Results display

Visualization Panel

  • Top: 2D contour plots with particle trajectories
  • Bottom: 3D surface plots showing optimization landscape
  • Real-time updates during optimization

Benchmark Functions

2D Functions

Function Description Global Minimum
Sphere f(x,y) = x² + y² (0,0)
Rosenbrock f(x,y) = 100(y-x²)² + (1-x)² (1,1)
Rastrigin Multi-modal function (0,0)
Ackley Many local minima (0,0)
Himmelblau Four equal minima Multiple

3D Functions

Function Dimensions Complexity
Sphere 3D 3 Unimodal
Rastrigin 3D 3 Multi-modal
Michalewicz 3 Many local minima
Levy 3D 3 Complex landscape

PSO Algorithm

Mathematical Formulation

Particle velocity and position updates:

v_i(t+1) = w * v_i(t) + c1 * r1 * (pbest_i - x_i(t)) + c2 * r2 * (gbest - x_i(t))
x_i(t+1) = x_i(t) + v_i(t+1)

Where:

  • w: Inertia weight
  • c1, c2: Cognitive and social parameters
  • r1, r2: Random numbers
  • pbest_i: Particle's best position
  • gbest: Global best position

Key Features

  • Boundary Handling: Particles bounce off boundaries
  • Velocity Clamping: Prevents explosion
  • History Tracking: Complete optimization history
  • Convergence Monitoring: Real-time best value tracking

Educational Value

This application serves as an excellent educational tool for:

  • Understanding PSO algorithm behavior
  • Visualizing optimization landscapes
  • Comparing benchmark function characteristics
  • Studying metaheuristic optimization
  • Learning about multi-modal optimization

Performance

Optimization Capabilities

  • Convergence: Rapid convergence on unimodal functions
  • Exploration: Effective global search on multi-modal functions
  • Stability: Robust performance across different landscapes
  • Scalability: Handles 2D and 3D problems efficiently

Visualization Performance

  • Smooth Animation: 30+ FPS particle movement
  • Interactive Plots: Zoom, pan, and rotate 3D views
  • Real-time Updates: Instant parameter feedback
  • Memory Efficient: Optimized for long runs

Use Cases

🎓 Education

  • Optimization algorithm courses
  • Metaheuristic visualization
  • Mathematical modeling classes

🔬 Research

  • Algorithm benchmarking
  • Parameter sensitivity analysis
  • Optimization landscape study

💼 Industry

  • Engineering optimization problems
  • Machine learning hyperparameter tuning
  • Financial modeling optimization

Contributing

We welcome contributions! Areas for improvement:

  • Additional benchmark functions
  • Advanced PSO variants
  • Export functionality
  • Performance optimizations
  • Additional visualization types

Citation

If you use this application in your research or teaching, please cite:

@software{pso_pyqt5_visualizer,
  title = {Particle Swarm Optimization PyQt5 Visualizer},
  author = {Martin Rivera},
  year = {2025},
  url = {https://huggingface.co/TroglodyteDerivations/pso-pyqt5-visualizer}
}

License

This project is licensed under the MIT License - see the LICENSE file for details.

Support

For issues and questions:

  • Open an issue on Hugging Face
  • Check the documentation
  • Review example configurations

Model Card Authors

[TroglodyteDerivations]

Model Card Contact

[https://huggingface.co/TroglodyteDerivations/Particle_Swarm_Optimization_Visualizer_PyQt5/edit/main/README.md]

✨ Watch particles find optimal solutions in beautiful visualizations! ✨

```

Additional Files for Hugging Face

You should also create these files for your Hugging Face repository:

README.md (same as above)

requirements.txt 

numpy>=1.21.0
matplotlib>=3.5.0
PyQt5>=5.15.0

app.py

LICENSE 

MIT License

Copyright (c) 2025 [Martin Rivera]

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

.gitattributes 

*.py filter=lfs diff=lfs merge=lfs -text
*.png filter=lfs diff=lfs merge=lfs -text
*.jpg filter=lfs diff=lfs merge=lfs -text

This model card provides comprehensive documentation for your PSO PyQt5 application and makes it ready for sharing on Hugging Face Hub!

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