Utilizing the Bees Algorithm and Machine Learning to optimize the UAVs travel time in consideration of Weather Conditions

Introduction

This repository contains the implementation of the Bees algorithm designed for UAV routing optimisation. The project demonstrates the optimization taking into account various external and weather factors.

Table of Contents

• Installation
• Dataset
• File Structure
• Usage
• Status
• Contributors

Installation

To set up the environment and install the required dependencies, navigate to the project directory and activate the virtual environment:

source myenv/bin/activate

Dataset

A dummy dataset was used for this project, which is based on previous weather data. This dataset serves to demonstrate the working of the bee-inspired optimization algorithm for UAV speed prediction.

File Structure

• myenv/: Contains the virtual environment with necessary libraries and dependencies.
• Optimised_Route_Plots(CBA-ML)/: Directory with optimized route plots using the CBA-ML approach.
• Optimised_Route_Plots(CBA)/: Directory with optimized route plots using the CBA approach.
• Reports/: Contains project reports and relevant documentation.
• Weather_Data/: Directory with weather data files.
• BA_C.ipynb: Jupyter notebook with the initial code and development details.
• bee_algorithm.py: Implementation of the bee-inspired optimization algorithm.
• data_module.py: Handles data loading, preprocessing, and splitting.
• main.py: Main execution script to run the entire process.
• visualization.py: Module for visualizing the optimization results.

Usage

To run the main script and execute the entire process:

python main.py

Status

Contributors

Authors

• @VedantZope