Multi-agent task allocation method based on the cost-effectiveness maximization multi-round auction algorithm

Multi-agent task allocation plays a crucial role in achieving efficient collaboration in heterogeneous multi-agent systems, especially in complex and dynamic environments. However, existing auction-based task allocation approaches often focus primarily on economic optimization or bid-oriented allocation while insufficiently considering the compatibility between agent capabilities and task attribute requirements, along with the overall cost-effectiveness from the task owner’s perspective. To address these limitations, in this paper, we propose a task allocation framework, which integrates task fitness modeling with cost-effectiveness maximization, and further develop a distributed multi-round auction mechanism. In particular, a task fitness model is constructed to quantitatively evaluate the suitability of agents for different tasks by combining multiple capability dimensions, where the importance of different task attributes is determined using the analytic hierarchy process (AHP). Based on this, a cost-effectiveness metric is defined by jointly considering agent bids and task fitness, and a multi-round auction algorithm, with dynamic bidding and an improved payment rule, is designed to maximize the overall task cost-effectiveness while ensuring incentive compatibility and individual rationality. Extensive simulation results demonstrate that the proposed approach significantly improves task cost-effectiveness and maintains high task execution suitability compared with conventional first-price, second-price, and existing multi-round auction mechanisms.