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Ma et al. Intell Robot 2023;3(4):581-95 Intelligence & Robotics
DOI: 10.20517/ir.2023.33

Research Article Open Access

Heterogeneous multi-agent task allocation based on
graph neural network ant colony optimization algorithms

Ziyuan Ma, Huajun Gong
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210000, Jiangsu, China.

Correspondence to: Prof. Huajun Gong, College of Automation Engineering, Nanjing University of Aeronautics and Astronautics,
Nanjing 210000, Jiangsu, China. E-mail: ghj301@nuaa.edu.cn
How to cite this article: Ma Z, Gong H. Heterogeneous multi-agent task allocation based on graph neural network ant colony
optimization algorithms. Intell Robot 2023;3(4):581-95. http://dx.doi.org/10.20517/ir.2023.33
Received: 1 Aug 2023 First Decision: Revised: 18 Oct 2023 Accepted: 24 Oct 2023 Published: 31 Oct 2023

Academic Editor: Simon X. Yang, Haibin Duan Copy Editor: Yanbin Bai Production Editor: Yanbin Bai

Abstract
Heterogeneous multi-agent task allocation is a key optimization problem widely used in fields such as drone swarms
and multi-robot coordination. This paper proposes a new paradigm that innovatively combines graph neural networks
and ant colony optimization algorithms to solve the assignment problem of heterogeneous multi-agents. The paper
introduces an innovative Graph-based Heterogeneous Neural Network Ant Colony Optimization (GHNN-ACO) algo-
rithm for heterogeneous multi-agent scenarios. The multi-agent system is composed of unmanned aerial vehicles,
unmanned ships, and unmanned vehicles that work together to effectively respond to emergencies. This method uses
graph neural networks to learn the relationship between tasks and agents, forming a graph representation, which is
then integrated into ant colony optimization algorithms to guide the search process of ants. Firstly, the algorithm
in this paper constructs heterogeneous graph data containing different types of agents and their relationships and
uses the algorithm to classify and predict linkages for agent nodes. Secondly, the GHNN-ACO algorithm performs
effectively in heterogeneous multi-agent scenarios, providing an effective solution for node classification and link pre-
diction tasks in intelligent agent systems. Thirdly, the algorithm achieves an accuracy rate of 95.31% in assigning
multiple tasks to multiple agents. It holds potential application prospects in emergency response and provides a new
idea for multi-agent system cooperation.

Keywords: Graph isomerism, neural network, enhanced ant colony optimization algorithms, heterogeneous multi-
agent, task allocation

© The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0
International License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, shar-
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