Abstract
Task allocation is an important issue in multi-agent systems, and finding the optimal solution of task allocation has been demonstrated to be an NP-hard problem. In many scenarios, agents are equipped with not only communication resources but also computing resources, so that tasks can be allocated and executed more efficiently in a distributed and parallel manner. Presently, many methods have been proposed for distributed task allocation in multi-agent systems. Most of them are either based on complete/full search or local search, and the former usually can find the optimal solutions but requires high computational cost and communication cost; the latter is usually more efficient but could not guarantee the solution quality. Evolutionary algorithm (EA) is a promising optimization algorithm which could be more efficient than the full search algorithms and might have better search ability than the local search algorithms, but it is rarely applied to distributed task allocation in multi-agent systems. In this paper, we propose a distributed task allocation method based on EA. We choose the many-objective EA called NSGA-III to optimize four objectives (i.e., maximizing the number of successfully allocated and executed tasks, maximizing the gain by executing tasks, minimizing the resource cost, and minimizing the time cost) simultaneously. Experimental results show the effectiveness of the proposed method, and compared with the full search strategy, the proposed method could solve task allocation problems with more agents and tasks.
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This work was partially supported by the National Natural Science Foundation of China (Grant No. 71701208).
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Zhou, J., Zhao, X., Zhao, D., Lin, Z. (2019). Task Allocation in Multi-agent Systems Using Many-objective Evolutionary Algorithm NSGA-III. In: Zhai, X., Chen, B., Zhu, K. (eds) Machine Learning and Intelligent Communications. MLICOM 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 294. Springer, Cham. https://doi.org/10.1007/978-3-030-32388-2_56
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DOI: https://doi.org/10.1007/978-3-030-32388-2_56
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