Evolutionary Multi-Objective Optimization Algorithms (EMOA) Benchmarking

This project implements and compares various Evolutionary Multi-Objective Optimization Algorithms (EMOA) using the pymoo framework. The implementation includes benchmarking of popular algorithms against well-known test problems and provides an interactive visualization platform.

Algorithms Implemented

The following state-of-the-art EMOAs are evaluated:

• NSGA-II (Non-dominated Sorting Genetic Algorithm II)
• NSGA-III (Non-dominated Sorting Genetic Algorithm III)
• MOEA/D (Multi-objective Evolutionary Algorithm Based on Decomposition)
• R-NSGA-II (Reference Point Based NSGA-II)
• SPEA2 (Strength Pareto Evolutionary Algorithm 2)

Test Problems

The algorithms are tested against the following benchmark problems:

• ZDT Test Suite:
  • ZDT1 (Convex Pareto Front)
  • ZDT2 (Non-convex Pareto Front)
  • ZDT3 (Disconnected Pareto Front)
• DTLZ Test Suite:
  • DTLZ1 (Linear Pareto Front)
  • DTLZ2 (Spherical Pareto Front)

Technologies Used

• Backend:

  • Python 3.8+
  • pymoo (Optimization Framework)
  • Flask (Web Framework)
  • NumPy (Numerical Computations)
  • pandas (Data Management)

• Frontend:

  • HTML5
  • CSS3
  • JavaScript
  • Plotly.js (Interactive Visualizations)

Installation

# Clone the repository
git clone https://github.com/yourusername/MOObyMOEA.git
cd MOObyMOEA.git

# Create and activate virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install required packages
pip install -r requirements.txt

Usage

1. Start the Flask server:

python3 app.py

2. Access the web interface at http://localhost:5000

3. Select the desired:

   • Algorithm
   • Test problem
   • Number of generations
   • Population size
   • Other algorithm-specific parameters

4. View and interact with the results through the visualization dashboard

Project Structure

emoa-benchmarking/
├── app.py                 # Flask application
├── requirements.txt       # Project dependencies
├── static/               # Static files
│   ├── css/             # Stylesheets
│   ├── js/              # JavaScript files
│   └── images/          # Images and icons
├── templates/           # HTML templates
├── algorithms/          # EMOA implementations
├── problems/           # Test problem implementations
└── utils/              # Utility functions

Features

• Interactive comparison of different EMOAs
• Real-time visualization of:
  • Pareto fronts
  • Convergence metrics
  • Population diversity
• Performance metrics calculation:
  • Hypervolume
  • IGD (Inverted Generational Distance)
  • Spread
• Export results in various formats (CSV, JSON)
• Customizable algorithm parameters
• Responsive web interface

Contributing

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

Acknowledgments

• pymoo development team
• Multi-objective optimization research community
• Flask and Plotly.js contributors