A Distributed Method for Solving the Heterogeneous Task Allocation Problem Considering Payload

This paper proposes a distributed heterogeneous task allocation method that considering payload. The method integrates the distributed matching-by-cloning hungarian-based algorithm (DMCHBA) and the decentralized genetic algorithm (GA), referred to as GA-DMCHBA. Improvements have been made to the agent cloning process and the Hungarian cost matrix generation in DMCHBA, as well as to the population iteration process in GA. These enhancements effectively solve the conflict-free task allocation problem that takes into account resource quantities and types. The algorithm initializes the genetic algorithm's population using DMCHBA, providing a better initial execution plan for the multi-agent system and facilitating the genetic algorithm's optimization during task execution. The proposed GA-DMCHBA method has been proven to efficiently solve the distributed heterogeneous task allocation problem considering payload within a limited time. In addition, the proposed method is compared with several existing approaches, further demonstrating its effectiveness and the efficiency of the proposed improvements.