Elevator Group Scheduling by Improved Dayan Particle Swarm Algorithm in Computer Cloud Computing Environment

Jie Yu, Bo Hu


The world is entering the era of cloud computing. Due to the rapid development of computer technology, as the core content of elevator transportation technology, elevator group control dispatching systems and group intelligent algorithms will have a wide range of application prospects due to their significant advantages. The purpose of this paper is to study the elevator group scheduling problem of the improved Dayan particle swarm algorithm in the computer cloud computing environment.This article first summarizes the research status of elevator group control technology and algorithms, and then analyzes and studies the basic theory of cloud computing task scheduling. Combined with the improved Dayan particle swarm algorithm, the elevator prediction model is established. This paper systematically expounds the theory and algorithm principle of the basic particle swarm algorithm, and analyzes the Dayan particle swarm algorithm on this basis. In this paper, the experimental research is carried out by comparing the two algorithms on the simulation software. Research shows that the improved Dayan particle swarm algorithm has better scheduling performance than the traditional basic particle swarm algorithm.

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Cloud Computing; Dayan Particle Swarm Algorithm; Elevator Group Scheduling; Application Research

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Zuntong W, Dong S, Fei Q, et al. Multi-objective scheduling algorithm for distributed elevator group control system. Control Theory and Application, 2020, 27(5 ):602-608.

Strang T , Bauer C . Context-Aware Group Elevator Scheduling. Elevator World, 2016, 54(11):58-66.

Yuhu C , Xuesong W , Yiyang Z . A Bayesian Reinforcement Learning Algorithm Based on Abstract States for Elevator Group Scheduling Systems. Chinese Journal of Electronics, 2020(3):394-398.

Zhang J, Zong Q. Group Elevator Peak Scheduling Based on Robust Optimization Model. Neuroscience Letters, 2016, 398( 3):268-273.

Chen Yuxian, Luo Sanding. A novel elevator scheduling algorithm based on information fusion% A novel elevator scheduling algorithm based on information fusion. Computer Engineering and Science, 2017, 035(012):178-184.

Zhu Dewen. The Optimal Scheduling Based On Ant Colony Algorithm For Elevator Group Control System. China Elevator, 2017, 026(4):39-43.

Hiller B , Klug T , Tuchscherer A . An exact reoptimization algorithm for the scheduling of elevator groups. Flexible Services and Manufacturing Journal, 2018, 26(4):585-608.

Lang Man, Li Guoyong, Xu Chenchen. Elevator Group Control System for Energy Scheduling Optimization Simulation. Computer Simulation, 2017, 034(002):375-379.

Serpen G , Debnath J . Design and performance evaluation of a parking management system for automated, multi-story and robotic parking structure. International Journal of Intelligent Computing & Cybernetics, 2019, 12(4):444-465.

Liu G , Zhou M , Jiang C . Petri Net Models and Collaborativeness for Parallel Processes with Resource Sharing and Message Passing. ACM Transactions on Embedded Computing Systems, 2017, 16(4):1-20.

Thirunavukkarasu G S , Krishna R . Scheduling algorithm for real-time embedded control systems using arduino board. KnE Engineering, 2017, 2(2):258.

Wang F, D Wang, Liu J. High-rise structure monitoring with elevator-assisted wireless sensor networking: design, optimization, and case study. Wireless Networks, 2019, 25(1):29-47.


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