Abstract
Task allocation is a key issue in multi-agent systems, and finding the optimal strategy for task allocation has been proved to be an NP-hard problem. Existing task allocation methods for multi-agent systems mainly adopt distributed full search strategies or local search strategies. The former requires a lot of computation and communication costs, while the latter cannot ensure the diversity and quality of solutions. Therefore, in this paper, we combine a distributed many-objective evolutionary algorithm called D-NSGA3 with a greedy algorithm to search the task allocation solutions, and we comprehensively consider the constraints related to space, time, energy consumption and agent function in multi-agent systems. Specifically, D-NSGA3 is used to optimize multiple objectives simultaneously so as to ensure the search capability and the diversity of solutions. Alternate search between D-NSGA3 and the greedy algorithm is conducted to enhance the local optimizing ability. Experimental results show that the proposed method can effectively solve large-scale task allocation problems (e.g., the number of agents is not less than 250, and that of tasks is not less than 1000). Compared with the existing work called MSEA, the proposed method could achieve better and more diverse solutions.
Highlights
Internet of Things and Artificial Intelligence technologies have made great progress in the past decade, and multi-agent systems [1] begin to be widely employed in realworld applications, such as unmanned systems [2], intelligent distributed traffic signal control systems [3], UAV formation combat systems [4], social networks [5], smart manufacturing [6], collaborative fault diagnosis systems [7], and robot rescue systems [8]
In view of the above, this paper comprehensively considers constraints related to space, time, energy consumption and function, etc., in task allocation, and simultaneously optimizes four objectives, i.e. maximizing the number of successfully executed tasks, maximizing the benefits of performing tasks and minimizing task execution time and resource consumption
Our contributions are listed as follows: (1) We propose a greedy algorithm that is designed based on the chromosome structure used in D-NSGA3 for solving the task allocation problem in multi-agent systems
Summary
Internet of Things and Artificial Intelligence technologies have made great progress in the past decade, and multi-agent systems [1] begin to be widely employed in realworld applications, such as unmanned systems [2], intelligent distributed traffic signal control systems [3], UAV formation combat systems [4], social networks [5], smart manufacturing [6], collaborative fault diagnosis systems [7], and robot rescue systems [8]. Agents usually need to finish specific tasks, such as firefighting, excavation, obstacle clearing, crowd evacuation, rescue, and transportation of materials to designated locations. Task allocation is one of the most important issues in multi-agent systems and finding the optimal solutions for. The goal of task allocation is to optimize the performance or benefits of task execution, such as maximizing the number of successfully executed tasks and minimizing the time and resource consumption of task execution. In addition to optimizing objectives, a large number of constraints generally need to be satisfied during task allocation. G., a rescue task in the fire disaster needs to be completed in a short time, otherwise, victims may have been killed or saved, and the Different agents and tasks may distribute at different geographic locations, and tasks may only be valid in a certain time interval
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