# A Benchmark for Multi-UAV Task Assignment of an Extended Team Orienteering Problem

arXiv: https://arxiv.org/abs/2009.00363 · HTML: https://mlsanity.com/paper/2009.00363/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Sep 2020.

## Built on by (most cited first, 5 of 5)

- [TOP-Former: A Multi-Agent Transformer Approach for the Team Orienteering Problem](https://mlsanity.com/paper/2311.18662/) (2023)
- [Towards Practical Multi-Robot Hybrid Tasks Allocation for Autonomous Cleaning](https://mlsanity.com/paper/2303.06531/) (2023)
- [Demand Acceptance using Reinforcement Learning for Dynamic Vehicle Routing Problem with Emission Quota](https://mlsanity.com/paper/2603.13279/) (2026)
- [NaviFormer: A Deep Reinforcement Learning Transformer-like Model to Holistically Solve the Navigation Problem](https://mlsanity.com/paper/2604.16967/) (2026)
- [Enhancing Transformer-based Routing by Encoding Distance via Relative Positional Encoding](https://mlsanity.com/paper/2607.18909/) (2026)

## Abstract (arXiv)

A benchmark for multi-UAV task assignment is presented in order to evaluate different algorithms. An extended Team Orienteering Problem is modeled for a kind of multi-UAV task assignment problem. Three intelligent algorithms, i.e., Genetic Algorithm, Ant Colony Optimization and Particle Swarm Optimization are implemented to solve the problem. A series of experiments with different settings are conducted to evaluate three algorithms. The modeled problem and the evaluation results constitute a benchmark, which can be used to evaluate other algorithms used for multi-UAV task assignment problems.