An Adaptive Multi-granularity Dynamic Task Reallocation Method Based on CBBA

TY - GEN

T1 - An Adaptive Multi-granularity Dynamic Task Reallocation Method Based on CBBA

AU - Guo, Binhua

AU - Xin, Bin

AU - Yang, Yixi

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.

PY - 2026

Y1 - 2026

N2 - Aiming at the bottleneck of collaborative efficiency caused by multi-source heterogeneous dynamic disturbances (such as task addition, deletion/change, agent failure/online) in post-disaster rescue scenarios, this paper proposes an improved consensus bundling algorithm framework (CBBA-AMG) that integrates adaptive multi-granularity response and load balancing optimization. By constructing a time-resource composite load model and introducing a load balancing penalty term based on Jensen-Shannon divergence, the problem of uneven task distribution in the traditional CBBA algorithm in heterogeneous multi-agent systems is effectively alleviated. At the same time, an event-driven dynamic reallocation mechanism is designed to dynamically trigger differentiated response granularities (global reset/cluster collaboration/individual adjustment) based on the disturbance impact metric function I(ϵ) to minimize the impact of replanning on system stability. Simulation results show that the proposed load balancing mechanism reduces the Gini coefficient of task allocation by 3.9%, and the maximum-minimum load difference is reduced by 16%; the adaptive multi-granularity strategy is 6.9 times faster than the global reallocation response speed, and the cumulative task revenue is increased by 12.7%, significantly improving the task completion rate and robustness of the system in a dynamic environment.

AB - Aiming at the bottleneck of collaborative efficiency caused by multi-source heterogeneous dynamic disturbances (such as task addition, deletion/change, agent failure/online) in post-disaster rescue scenarios, this paper proposes an improved consensus bundling algorithm framework (CBBA-AMG) that integrates adaptive multi-granularity response and load balancing optimization. By constructing a time-resource composite load model and introducing a load balancing penalty term based on Jensen-Shannon divergence, the problem of uneven task distribution in the traditional CBBA algorithm in heterogeneous multi-agent systems is effectively alleviated. At the same time, an event-driven dynamic reallocation mechanism is designed to dynamically trigger differentiated response granularities (global reset/cluster collaboration/individual adjustment) based on the disturbance impact metric function I(ϵ) to minimize the impact of replanning on system stability. Simulation results show that the proposed load balancing mechanism reduces the Gini coefficient of task allocation by 3.9%, and the maximum-minimum load difference is reduced by 16%; the adaptive multi-granularity strategy is 6.9 times faster than the global reallocation response speed, and the cumulative task revenue is increased by 12.7%, significantly improving the task completion rate and robustness of the system in a dynamic environment.

KW - CBBA

KW - multi-agent system

KW - task redistribution

KW - uncertainty

UR - https://www.scopus.com/pages/publications/105030930149

U2 - 10.1007/978-981-95-6730-0_2

DO - 10.1007/978-981-95-6730-0_2

M3 - Conference contribution

AN - SCOPUS:105030930149

SN - 9789819567294

T3 - Communications in Computer and Information Science

SP - 15

EP - 30

BT - Advanced Computational Intelligence and Intelligent Informatics - 9th International Workshop, IWACIII 2025, Proceedings

A2 - Ma, Hongbin

A2 - Xin, Bin

A2 - She, Jinhua

A2 - Dai, Yaping

PB - Springer Science and Business Media Deutschland GmbH

T2 - 9th International Workshop on Advanced Computational Intelligence and Intelligent Informatics, IWACIII 2025

Y2 - 31 October 2025 through 4 November 2025

ER -