A Computing-for-Communication Method Without Additional Protocols and Traffic for Networked Multiagent Scheduling

Abstract

Multiagent scheduling has recently been reinvigorated by the burgeoning application of swarm, receiving significant attention due to its new characteristics. The market-based method is a fast distributed scheduling method that is naturally suitable for agent swarm, while its multiround communication is inevitably affected by the environment and the performance deteriorates. This article proposes an idea of computing-for-communication (CFC) with improving or even appropriately increasing computation to reduce communication rounds and improve the performance meanwhile, which does not add additional communication protocols and traffic but may moderately increase the amount of computation and storage. First, a new scoring function and a local optimization method are proposed to improve the agent’s schedule and resolve the conflict among agents in advance. Second, an agent location inference method and task-related agent selection strategy are presented for local optimization, which is expected to avoid the increase of communication in locations and the waste of computation on irrelevant agents. Third, some modifications for removing and adding tasks are proposed to further improve the performance of scheduling. Finally, extensive Monte Carlo experiments demonstrate the commendable performance of the proposed method in comparison with the representative consensus-based bundle algorithm (CBBA) and performance impact algorithm (PI).