Evolutionary task assignment in distributed multi-agent networks with local interactions

In distributed multi-agent networks with local interactions, effective resource allocation to attain multiple tasks is a key system performance. In this paper, a dynamic evolutionary task assignment approach for such networks is introduced. According to this novel approach, every agent will allocate resources according to its individually-assigned task prioritization as well as local interactions with neighboring agents. The effects of different parameters used in the approach are studied and tested by simulations. Relevant applications to robot networks are discussed. A comprehensive simulation-based study is provided to demonstrate the performance of the proposed approach.