Abstract
To improve the task execution efficiency of multi-Agent system (MAS), an intelligent task allocation method based on improved quantum particle swarm optimization (QPSO) algorithm is proposed. Firstly, the task allocation of MAS system is modeled, and the objective function is constructed by considering the ability and load of Agent. Then, the traditional QPSO algorithm is improved by incorporating chaotic mapping, Gaussian distribution mutation operator and dynamic inertia weighting technology to enhance the diversity of the population and make it have stronger search ability. Finally, the improved QPSO algorithm is used to optimize the task allocation model and get the best allocation scheme. Simulation results show that this method can shorten the task completion time and balance the system load.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Journal of Ambient Intelligence and Humanized Computing
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
