A Homogeneous Cloud Task Distribution Method Based on an Improved Leapfrog Algorithm

Huo, Yunliang; Xiong, Ji; Guo, Zhixing; You, Qianbing; Yi, Peng

doi:10.32604/cmes.2021.015314

Cited by 2 publications

(3 citation statements)

References 33 publications

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“…A grey relational degree is a weighted sum of the grey relational coefficients obtained via Eq. (15).…”

Section: Gray Correlation-based Case Similaritymentioning

confidence: 99%

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Case Retrieval Strategy of Turning Process Based on Grey Relational Analysis

Zhao,

Huo,

Xiong

et al. 2024

CMES

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To solve the problem of long response time when users obtain suitable cutting parameters through the Internet based platform, a case-based reasoning framework is proposed. Specifically, a Hamming distance and Euclidean distance combined method is designed to measure the similarity of case features which have both numeric and category properties. In addition, AHP (Analytic Hierarchy Process) and entropy weight method are integrated to provide features weight, where both user preferences and comprehensive impact of the index have been concerned. Grey relation analysis is used to obtain the similarity of a new problem and alternative cases. Finally, a platform is also developed on Visual Studio 2015, and a case study is demonstrated to verify the practicality and efficiency of the proposed method. This method can obtain cutting parameters which is suitable without iterative calculation. Compared with the traditional PSO (Particle swarm optimization algorithm) and GA (Genetic algorithm), it can obtain faster response speed. This method can provide ideas for selecting processing parameters in industrial production. While guaranteeing the characteristic information is similar, this approach can select processing parameters which is the most appropriate for the production process and a lot of time can be saved.

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“…A grey relational degree is a weighted sum of the grey relational coefficients obtained via Eq. (15).…”

Section: Gray Correlation-based Case Similaritymentioning

confidence: 99%

“…However, with the development of the network-based public service manufacturing model [14][15][16], the above methods are not applicable anymore because too much time is consumed. It is unacceptable for users if the response time is too long when they access the Internet to obtain service.…”

Section: Introductionmentioning

confidence: 99%

Case Retrieval Strategy of Turning Process Based on Grey Relational Analysis

Zhao,

Huo,

Xiong

et al. 2024

CMES

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show abstract

“…Currently, with the in-depth study of intelligent optimization algorithms, numerous studies have explored the applications of intelligent optimization algorithms in practical industrial problems. 13 Previously, a new calibration model combining the robot kinematics model and genetic algorithm, without measuring the absolute pose of the moving platform was proposed. 14 The update strategy of the original beetle swarm optimization algorithm was modified using the preference value to perform random replacement and a calibration algorithm was proposed on this basis.…”

Section: Introductionmentioning

confidence: 99%

Kinematic parameters calibration of industrial robot based on RWS-PSO algorithm

Zhang

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part C:

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The positioning accuracy of an industrial robot has a significant impact on its application in precision manufacturing, and it is necessary to calibrate robot kinematic parameters. Previous studies have established numerous nonlinear equations to solve the kinematic parameters, which are complicated and time consuming. A standard particle swarm optimization (PSO) algorithm is limited by long running time and low solution efficiency. Therefore, in this study, a dynamic particle swarm optimization algorithm based on roulette wheel selection (RWS-PSO) is proposed to realize the kinematic parameters calibration. First, a kinematics model is constructed using the standard Denavit-Hartenberg (D-H) method, and the theoretical and actual values of the spatial position of the robot endpoint are obtained via forward kinematics and a Laser Tracker, respectively. Next, the kinematic parameters calibration problem is transformed into a solution of a high-dimensional nonlinear equation using the proposed RWS-PSO algorithm. In the proposed RWS-PSO algorithm, the inertia factor is considered as linearly decreasing and the number of particles is selected by the roulette wheel selection (RWS) to improve its computational efficiency. The proposed RWS-PSO and standard PSO algorithms are compared based on various indices by simulation. The results reveal that the time cost of the RWS-PSO algorithm is much lower than that of the standard PSO algorithm on the basis of high precision and a reduced running time of approximately 53%. Finally, the kinematic parameter errors obtained by the two algorithms are compensated. According to the experimental results, the positioning accuracy of the robot in three ( x-, y-, and z-) directions is improved by 78%, 46%, and 67%, respectively, compared to that of before compensation, which proves that the RWS-PSO algorithm is effective and practical for kinematic parameters calibration.

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A Homogeneous Cloud Task Distribution Method Based on an Improved Leapfrog Algorithm

Cited by 2 publications

References 33 publications

Case Retrieval Strategy of Turning Process Based on Grey Relational Analysis

Case Retrieval Strategy of Turning Process Based on Grey Relational Analysis

Kinematic parameters calibration of industrial robot based on RWS-PSO algorithm

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