# Large-Scale Multi-UAV Task Allocation via a Centrality-Driven Load-Aware Adaptive Consensus Bundle Algorithm for Biomimetic Swarm Coordination

## TL;DR

This paper introduces a new algorithm for coordinating large groups of drones that improves efficiency and communication by mimicking natural swarm behaviors.

## Contribution

The novel CLAC-CBBA algorithm uses biomimetic principles to enhance task allocation in large heterogeneous UAV swarms.

## Key findings

- CLAC-CBBA reduces communication overhead and runtime in large UAV swarms.
- The algorithm improves total task score compared to existing methods.
- Results show CLAC-CBBA is scalable and robust for diverse network densities and swarm sizes.

## Abstract

Large multi-UAV mission systems operate over time-varying communication graphs with heterogeneous platforms, where classical distributed task assignment may incur excessive message passing and suboptimal task–resource matching. To address these challenges, this paper proposes CLAC-CBBA (Centrality-Driven and Load-Aware Adaptive Clustering CBBA), an enhanced variant of the Consensus-Based Bundle Algorithm (CBBA) for large heterogeneous swarms. The proposed method is biomimetic in the sense that it integrates swarm-inspired self-organization and load-aware self-regulation to improve scalability and robustness, resembling decentralized role emergence and negative-feedback workload balancing in natural swarms. Specifically, CLAC-CBBA first identifies key nodes via a centrality-based adaptive cluster-reconfiguration mechanism (CenCluster) and partitions the network into cooperation domains to reduce redundant communication. It then applies a load-aware cluster self-regulation mechanism (LCSR), which combines resource attributes and spatial information, uses K-medoids clustering, and triggers split/merge reconfiguration based on real-time load imbalance. CBBA bidding is executed locally within clusters, while anchors and cluster representatives synchronize winners/bids to ensure globally consistent, conflict-free assignments. Simulations across diverse network densities and swarm sizes show that CLAC-CBBA reduces communication overhead and runtime while improving total task score compared with CBBA and several advanced variants, with statistically significant gains. These results demonstrate that CLAC-CBBA is scalable and robust for large-scale heterogeneous UAV task allocation.