Solve mTSP with pymoo

Leveraging the pymoo framework, our package solves mTSP problems through the adept application of genetic algorithms -- NSGA-II. By constraining the original model designated for the ST-SR-TA MRTA problem, we have honed a tool capable of unraveling the intricacies of mTSP.

Background

In its nascent stage, the primary objective of this project was to unlock solutions for the Single-Task Single-Robot Task-Allocation (ST-SR-TA) Multi-Robot Task Allocation (MRTA) issue. The journey towards achieving this milestone beckoned a reconfiguration of our strategy to focus on the mTSP problem, a well-established benchmark in the world of optimization.

Getting Started

To dive in, visit our installation guide and documentation to set up the environment and acquaint yourself with the operational aspects of the package.

Prerequisite: Install poetry

curl -sSL https://install.python-poetry.org | python3 -

Clone this repo

git clone https://github.com/airuchen/mrta_pymoo.git

Install dependencies and activate the virtual env
```
cd mrta_pymoo/
poetry install
poetry shell
```
Run the Benchmark by specifying the desired tsp dataset and the number of the robots.
```
python3 ./mrta_pymoo/main.py
```

Potential Applications

Beyond serving as a benchmarking tool, the potential of this package unfurls in various domains including logistics, supply chain management, and robotic path planning, offering solutions characterized by cost-efficiency and optimal routing.

License

This project is licensed under the MIT License.