A chaos wolf optimization algorithm with self-adaptive variable step-size

Zhu, Yong; Jiang, Wanlu; Kong, Xianwen; Quan, Lingxiao; Zhang, Yongshun

doi:10.1063/1.5005130

Cited by 12 publications

(11 citation statements)

References 28 publications

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“…Due to the availability of big data technology and data mining methods as well as the emergence of new IIoT platforms and machine learning algorithms, fault diagnosis for hydraulic valves based on big data for hydraulic system with condition monitoring is one of the focuses for this research [26][27][28]. Among them, Principal Component Analysis (PCA) is an effective method for dimensionality reduction in big data analysis.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis Method for Hydraulic Directional Valves Integrating PCA and XGBoost

Lei

Jiang

et al. 2019

Processes

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A novel fault diagnosis method is proposed, depending on a cloud service, for the typical faults in the hydraulic directional valve. The method, based on the Machine Learning Service (MLS) HUAWEI CLOUD, achieves accurate diagnosis of hydraulic valve faults by combining both the advantages of Principal Component Analysis (PCA) in dimensionality reduction and the eXtreme Gradient Boosting (XGBoost) algorithm. First, to obtain the principal component feature set of the pressure signal, PCA was utilized to reduce the dimension of the measured inlet and outlet pressure signals of the hydraulic directional valve. Second, a machine learning sample was constructed by replacing the original fault set with the principal component feature set. Third, the MLS was employed to create an XGBoost model to diagnose valve faults. Lastly, based on model evaluation indicators such as precision, the recall rate, and the F1 score, a test set was used to compare the XGBoost model with the Classification And Regression Trees (CART) model and the Random Forests (RFs) model, respectively. The research results indicate that the proposed method can effectively identify valve faults in the hydraulic directional valve and have higher fault diagnosis accuracy.

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Section: Introductionmentioning

confidence: 99%

Fault Diagnosis Method for Hydraulic Directional Valves Integrating PCA and XGBoost

Lei

Jiang

et al. 2019

Processes

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“…Test Function. In order to test the performance of the CDWPA proposed in this paper, we select 10 standard test functions in [31] (dimensions from 2 to 200) to conduct the first set of experiments, and compare the optimization performance with wolf pack algorithm (WPA), oppositional wolf pack algorithm (OWPA) in [24], chaotic wolf pack algorithm (CWPA) in [22], PSO, ABC, and ASFA [32]. In order to further verify the ability of the improved algorithm to solve high-dimensional complex functions, the second set of experiments was carried out.…”

Section: Simulation Experiments and Algorithmmentioning

confidence: 99%

“…e study in [21] makes use of tent chaotic mapping method to improve the quality of initial solution to the wolf pack algorithm, which endows the WPA with faster convergence speed and higher solution accuracy. In [22], a chaos optimization method based on logistic map was used to initialize the population, which improved the optimization accuracy and convergence rate of WPA. In [23], the d-dimensional chaotic variables were mapped to the solution space to obtain the initial wolf group.…”

Section: Introductionmentioning

confidence: 99%

A Chaotic Disturbance Wolf Pack Algorithm for Solving Ultrahigh‐Dimensional Complex Functions

Zhu¹,

Wu²,

Li³

et al. 2021

Complexity

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The optimization of high-dimensional functions is an important problem in both science and engineering. Wolf pack algorithm is a technique often used for computing the global optimum of a multivariable function. In this paper, we develop a new wolf pack algorithm that can accurately compute the optimal value of a high-dimensional function. First, chaotic opposite initialization is designed to improve the quality of initial solution. Second, the disturbance factor is added in the scouting process to enhance the searching ability of wolves, and an adaptive step length is designed to enhance the global searching ability to prevent wolves from falling into the local optimum effectively. A set of standard test functions are selected to test the performance of the proposed algorithm, and the test results are compared with other algorithms. The high-dimensional and ultrahigh-dimensional functions (500 and 1000) are tested. The experimental results show that the proposed algorithm features in good global convergence, high accuracy calculation, strong robustness, and excellent performance in high-dimensional functions.

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“…In [25], to explore the problem of parameter optimization for complex nonlinear function, a chaos wolf optimization algorithm (CWOA) with self-adaptive variable step size was proposed. In [26], Mirjalili et al proposed "grey wolf optimizer" based on the cooperative hunting behaviour of wolves, which can be regarded as a variant of paper [19]; in [27][28][29], grey wolf optimizer is adopted to solve nonsmooth optimal power flow problems, optimal planning of renewable distributed generation in distribution systems, and optimal reactive power dispatch considering SSSC (static synchronous series compensator).…”

Section: Literature Reviewmentioning

confidence: 99%

An Adaptive Shrinking Grid Search Chaotic Wolf Optimization Algorithm Using Standard Deviation Updating Amount

Wang

Ban

et al. 2020

Computational Intelligence and Neuroscience

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To improve the optimization quality, stability, and speed of convergence of wolf pack algorithm, an adaptive shrinking grid search chaotic wolf optimization algorithm using standard deviation updating amount (ASGS-CWOA) was proposed. First of all, a strategy of adaptive shrinking grid search (ASGS) was designed for wolf pack algorithm to enhance its searching capability through which all wolves in the pack are allowed to compete as the leader wolf in order to improve the probability of finding the global optimization. Furthermore, opposite-middle raid method (OMR) is used in the wolf pack algorithm to accelerate its convergence rate. Finally, “Standard Deviation Updating Amount” (SDUA) is adopted for the process of population regeneration, aimed at enhancing biodiversity of the population. The experimental results indicate that compared with traditional genetic algorithm (GA), particle swarm optimization (PSO), leading wolf pack algorithm (LWPS), and chaos wolf optimization algorithm (CWOA), ASGS-CWOA has a faster convergence speed, better global search accuracy, and high robustness under the same conditions.

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A chaos wolf optimization algorithm with self-adaptive variable step-size

Cited by 12 publications

References 28 publications

Fault Diagnosis Method for Hydraulic Directional Valves Integrating PCA and XGBoost

Fault Diagnosis Method for Hydraulic Directional Valves Integrating PCA and XGBoost

A Chaotic Disturbance Wolf Pack Algorithm for Solving Ultrahigh‐Dimensional Complex Functions

An Adaptive Shrinking Grid Search Chaotic Wolf Optimization Algorithm Using Standard Deviation Updating Amount

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