文献类型：期刊文献

英文题名：Distributed Task Allocation for Multi-Agent Systems: A Submodular Optimization Approach

作者：Liu, Jing[1]; Li, Fangfei[2]; Jin, Xin[3,4]; Tang, Yang[4]

机构：[1] East China University of Science and Technology, School of Mathematics, Shanghai, 200237, China; [2] East China University of Science and Technology, School of Mathematics, Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, Shanghai, 200237, China; [3] Fudan University, Research Institute of Intelligent Complex Systems, Shanghai, 200433, China; [4] East China University of Science and Technology, Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, Shanghai, 200237, China

年份：2026

外文期刊名：IEEE Transactions on Automatic Control

收录：EI(收录号：20262020740965)

语种：英文

外文关键词：Approximation algorithms - Computational complexity - Computational efficiency - Distributed computer systems - Intelligent agents - Intelligent systems - Monte Carlo methods - Optimization - Parallel algorithms - Polynomial approximation

摘要：This paper addresses dynamic task allocation in resource-constrained multi-agent systems (MASs) with sequentially updated assignments. We develop a submodular maximization framework integrated with q-independence systems, demonstrating greater flexibility than conventional matroid-based constraints for modeling heterogeneous resource limitations. The proposed distributed greedy bundles algorithm (DGBA) addresses communication limitations in MASs while providing rigorous approximation guarantees for submodular maximization under a q-independence system constraint, ensuring low computational complexity. DGBA achieves feasible task allocation in polynomial time with reduced space complexity compared to existing methods. Extensive Monte Carlo simulations in a micro-satellite observation scenario demonstrate that DGBA consistently outperforms benchmark algorithms in total utility, resource efficiency, and assignment stability, while maintaining real-time computational feasibility. ? 1963-2012 IEEE.