https://github.com/suleimanelkhoury/ec-scheduler

Three implemented evolutionary strategies using DEAP to optimize energy scheduling tasks.
https://github.com/suleimanelkhoury/ec-scheduler

cmaes deap docker evolutionary-algorithms flask machine-learning microservices nsga-ii pso python redis

Last synced: 7 months ago
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Three implemented evolutionary strategies using DEAP to optimize energy scheduling tasks.

• Host: GitHub
• URL: https://github.com/suleimanelkhoury/ec-scheduler
• Owner: suleimanelkhoury
• Created: 2024-08-07T00:29:24.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-09-09T12:10:16.000Z (about 1 year ago)
• Last Synced: 2025-01-21T23:15:06.918Z (9 months ago)
• Topics: cmaes, deap, docker, evolutionary-algorithms, flask, machine-learning, microservices, nsga-ii, pso, python, redis
• Language: Python
• Homepage:
• Size: 45.9 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# **EC-Scheduler**

## **Project Overview**

Welcome to **EC-Scheduler**! This project provides a comprehensive solution for optimizing and scheduling general energy resources. It consists of three main components:

1. **Optimization Method (`ec-scheduler`)**:

   This part of our work implements three various evolutionary algorithms using the DEAP (Distributed Evolutionary Algorithms in Python) library. It includes:

   - **`NSGA2.py`** : Non-dominated Sorting Genetic Algorithm II.

   - **`PSO.py`** : Particle Swarm Optimization.

   - **`CMAES.py`** : Covariance Matrix Adaptation Evolution Strategy.

   - **`ea_utils.py`**: This file contains the helper, statistics, plotting, and parallelization functionalities for each evolutionary algorithms.

   - **`Strategy.py`**: This file contains the strategy logic for generating and updating the population in CMA-ES.

   - **`genotyp_phenotyp.py`**: This file contains logic used to convert chromosomes into the corresponding scheduling plans, and send and receive the values to and from the Mockup Scheduler.

   - **`main.py`**: This file contains the main Flask app for the project.

   - **`container_handling.py`**: This file contains the logic for handling the containers on the IAI cluster using Kubernetes.

2. **Mockup Scheduler (`mockup_scheduler`)**:

   This component simulates the scheduling of five different types of energy facilities:

   - Photovoltaic (solar panels)

   - Wind Turbine

   - Two Batteries

   - Combined Heat and Power Plant

3. **User Interface (`user_interface`)**:

   Built using PyQt5, this component provides a graphical interface to interact with the optimization methods. It allows users to configure and execute optimization tasks and view results.

## **Getting Started**

To get started with **ec-scheduler**, simply execute the `start_services.sh` script to build and run the necessary Docker containers. This script handles the setup and execution of all services required for the project.

## **Project Structure**

- **`start_services.sh`**: Script to build and run Docker containers.

- **`delete_services.sh`**: Script to delete Docker containers.

- **`optimization_method`**: Directory for the optimization methods.

- **`mockup_scheduler`**: Directory for a simple mockup scheduler project.

- **`user_interface`**: Directory for the PyQt5 UI.

## **Contributing**

Contributions are welcome! To contribute, please fork the repository and submit a pull request.