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All Journal International Journal of Industrial Optimization (IJIO) Journal of Applied Data Sciences
Yu, Jie
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Industrial & Manufacturing Engineering

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Elevator Group Scheduling by Improved Dayan Particle Swarm Algorithm in Computer Cloud Computing Environment Yu, Jie; Hu, Bo
Journal of Applied Data Sciences Vol 3, No 2: MAY 2022
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v3i2.60

Abstract

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.
Robust parametric optimization of cyclone separator by means of probabilistic multi - objective optimization Zheng, Maosheng; Yu, Jie
International Journal of Industrial Optimization Vol. 7 No. 1 (2026)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/ijio.v7i1.12050

Abstract

In this article, robust parametric optimization of cyclone separator is done by means of robust probabilistic multi - objective optimization (RPMOO). In RPMOO, the optimal attributes (objectives) are essentially divided into two types, i.e., both unbeneficial and beneficial types, which devote their partial preferable probabilities with equivalent manner quantitatively; especially the averaged value of the experimental data of each attribute and its dispersity are evaluated individually in accordance with its corresponding type. The total preferable probability of each scheme alternative is formed from the multiplication of all available partial preferable probabilities, which is the uniquely decisive indicator of an alternative in this assessment; the optimum scheme is with the highest total preferable probability. For the parametric optimization of cyclone separator, the inlet velocity, helical angle, and outlet diameter are as the variable parameters, while the pressure drop and separation efficiency are the evaluated responses of the cyclone separator to get optimization, the former is an unbeneficial type of attribute and the latter is a beneficial type of attribute. The orthogonal array L9(33) was employed to arrange the experimental scheme alternatives. The evaluated results indicate that the optimized experimental scheme is alternative 6, which yields the optimal responses of a pressure drop of 0.3 mba and a separation efficiency of 98.95 % at an optimum inlet velocity of 13 m/s, an outlet diameter of 72 mm, and a helical angle of 5. This work reveals the independent contributions of the averaged value of the experimental data and its dispersion to an attribute response in the optimization process, and the irrelevance of pressure drop and separation efficiency in the system.
Co-Authors Hu, Bo Zheng, Maosheng