Abstract
In order to solve the problem that the traditional particle swarm algorithm is difficult to converge to the optimal solution in the late iteration, and enhance the global search ability of traditional particle swarm algorithm, the inertial weights of the random perturbation sine adjustment particles were added at the initial and end of the search. At the same time, some Benchmark functions are used to test the improved particle swarm algorithm, and the results show that the improved particle swarm algorithm has obtained remarkable progress on convergence speed and precision. In this paper, the improved particle swarm optimization algorithm is utilized to optimize the whole system of central air conditioning and the optimal working point corresponding to minimum system energy consumption can be confirmed.
Sign-in/Register to access full text options 
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: IOP Conference Series: Earth and Environmental Science
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.
