Improving Asynchronous Search for Distributed Generalized Assignment Problem

Distributed Generalized Assignment Problem (D-GAP) is very popular in scalable multi-agent systems. However, existing algorithms are not effective or efficient in large scale or highly dynamic domains due to the limited communication and computation resource. In this paper, we present a novel approach to address this issue. To reduce communication, we propose a decentralized model for agents to jointly search for optimized solutions. Considering the complexity of D-GAP in a massive multi-agent system, agents cannot perform the optimal search based on their local views, we propose a heuristic algorithm. By inferring knowledge from their previous communicated searches, agents are able to predict how to deploy their future similar searches more efficiently. If an agent can solve some parts of D-GAP well, similar searches will be sent to it. By taking the advantage of the accumulation effect to agents' local knowledge, agents can independently make simple decisions with highly reliable performance and limited communication overheads. Finally, we present a simulation to demonstrate the feasibility and efficiency of our algorithm.