Evaluation of axis straightness error of shaft and hole parts based on improved grey wolf optimization algorithm

Song, Ci; Wang, Xibin; Liu, Zhibing; Chen, Hui

doi:10.1016/j.measurement.2021.110396

Cited by 11 publications

(5 citation statements)

References 24 publications

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“…Dokur et al [22] used GWO to solve a short-term wind speed-prediction problem with a multilayer perceptron, and the results showed that the algorithm was more effective than other algorithms. Song et al [23] used GWO to optimize the shaft straightness error assessment of shaft hole-type parts, and the results confirmed that the algorithm was more accurate in solving this problem.…”

Section: Intelligent Optimization Algorithmmentioning

confidence: 93%

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Research on Dynamic Economic Dispatch Optimization Problem Based on Improved Grey Wolf Algorithm

Yang,

Zhang,

Zhu

et al. 2024

Energies

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The dynamic economic dispatch (DED) problem is a typical complex constrained optimization problem with non-smooth, nonlinear, and nonconvex characteristics, especially considering practical situations such as valve point effects and transmission losses, and its objective is to minimize the total fuel costs and total carbon emissions of generating units during the dispatch cycle while satisfying a series of equality and inequality constraints. For the challenging DED problem, a model of a dynamic economic dispatch problem considering fuel costs is first established, and then an improved grey wolf optimization algorithm (IGWO) is proposed, in which the exploitation and exploration capability of the original grey wolf optimization algorithm (GWO) is enhanced by initializing the population with a chaotic algorithm and introducing a nonlinear convergence factor to improve weights. Furthermore, a simple and effective constraint-handling method is proposed for the infeasible solutions. The performance of the IGWO is tested with eight benchmark functions selected and compared with other commonly used algorithms. Finally, the IGWO is utilized for three different scales of DED cases, and compared with existing methods in the literature. The results show that the proposed IGWO has a faster convergence rate and better global optimization capabilities, and effectively reduces the fuel costs of the units, thus proving the effectiveness of IGWO.

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Section: Intelligent Optimization Algorithmmentioning

confidence: 93%

“…The first step is to roughly calculate the cost of each unit and form a set E in ascending order to evaluate the real-time efficiency of each unit. The calculation is shown as Equation (23).…”

Section: Power Balance Constraint Handlingmentioning

confidence: 99%

Research on Dynamic Economic Dispatch Optimization Problem Based on Improved Grey Wolf Algorithm

Yang,

Zhang,

Zhu

et al. 2024

Energies

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“…Some algorithms have improved GWO performance by modifying and adjusting parameters. Song et al [40] proposed IGWO, which enhanced exploration by modifying linear convergence factor to nonlinear; The improved grey wolf optimizer also adjusted a nonlinear parameter of GWO based on polynomials [41], and showed accurate measurement results in the optimization of seepage parameters; However, these nonlinear strategies have only succeeded in improving the performance of GWO in some aspects. For example, improved GWO [42] was beneficial to improve the convergence performance of unimodal functions, but has a poor effect on multimodal functions.…”

Section: Related Workmentioning

confidence: 99%

Improved GWO and its application in parameter optimization of Elman neural network

et al. 2023

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Traditional neural networks used gradient descent methods to train the network structure, which cannot handle complex optimization problems. We proposed an improved grey wolf optimizer (SGWO) to explore a better network structure. GWO was improved by using circle population initialization, information interaction mechanism and adaptive position update to enhance the search performance of the algorithm. SGWO was applied to optimize Elman network structure, and a new prediction method (SGWO-Elman) was proposed. The convergence of SGWO was analyzed by mathematical theory, and the optimization ability of SGWO and the prediction performance of SGWO-Elman were examined using comparative experiments. The results show: (1) the global convergence probability of SGWO was 1, and its process was a finite homogeneous Markov chain with an absorption state; (2) SGWO not only has better optimization performance when solving complex functions of different dimensions, but also when applied to Elman for parameter optimization, SGWO can significantly optimize the network structure and SGWO-Elman has accurate prediction performance.

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“…Song Yusheng et al used IGWO to optimize an SVM. They aimed at the problems of low efficiency and easily falling into a local optimum in GWO optimization and improved the traditional GWO model through nonlinear control and random weight position updating [24]. The test results showed that the model effectively improved the fault diagnosis efficiency and recognition rate.…”

Section: Introductionmentioning

confidence: 99%

Rolling Bearing Fault Diagnosis Based on Support Vector Machine Optimized by Improved Grey Wolf Algorithm

Shen

Xiao

Wang

et al. 2023

Sensors

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This study targets the low accuracy and efficiency of the support vector machine (SVM) algorithm in rolling bearing fault diagnosis. An improved grey wolf optimizer (IGWO) algorithm was proposed based on deep learning and a swarm intelligence optimization algorithm to optimize the structural parameters of SVM and improve the rolling bearing fault diagnosis. A nonlinear contraction factor update strategy was also proposed. The variable coefficient changes with the shrinkage factor α. Thus, the search ability was balanced at different early and late stages by controlling the dynamic changes of the variable coefficient. In the early stages of optimization, its speed is low to avoid falling into local optimization. In the later stages of optimization, the speed is higher, and finding the optimal solution is easier, balancing the two different global and local optimization capabilities to complete efficient convergence. The dynamic weight update strategy was adopted to perform position updates based on adaptive dynamic weights. First, the dataset of Case Western Reserve University was used for simulation, and the results showed that the diagnosis accuracy of IGWO-SVM was 98.75%. Then, the IGWO-SVM model was trained and tested using data obtained from the full-life-cycle test platform of mechanical transmission bearings independently researched and developed by Nanjing Agricultural University. The fault diagnosis accuracy and convergence value of the adaptation curve were compared with those of PSO-SVM (particle swarm optimization) and GWO-SVM diagnosis models. Results showed that the IGWO-SVM model had the highest rolling bearing fault diagnosis accuracy and the best diagnosis convergence.

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Evaluation of axis straightness error of shaft and hole parts based on improved grey wolf optimization algorithm

Cited by 11 publications

References 24 publications

Research on Dynamic Economic Dispatch Optimization Problem Based on Improved Grey Wolf Algorithm

Research on Dynamic Economic Dispatch Optimization Problem Based on Improved Grey Wolf Algorithm

Improved GWO and its application in parameter optimization of Elman neural network

Rolling Bearing Fault Diagnosis Based on Support Vector Machine Optimized by Improved Grey Wolf Algorithm

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