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

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) algorithm 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 prediction 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.