A new approach for estimating the parameters of Weibull distribution via particle swarm optimization: An application to the strengths of glass fibre data

Acıtaş, Şükrü; Aladağ, Çağdaş Hakan; Şenoğlu, Birdal

doi:10.1016/j.ress.2018.07.024

Cited by 51 publications

(19 citation statements)

References 38 publications

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“…Particle Swarm Optimization (PSO) developed by Eberhart, Kennedy [24] is based on swarm behaviour, such as bird flocking and fish schooling to find a place with enough food in nature. PSO has been extensively thought in various applied studies due to its easy implementation, high exactness, and fast convergence [25,26].…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

An Efficient Hybrid Algorithm with Particle Swarm Optimization and Nelder-Mead Algorithm for Parameter Estimation of Nonlinear Regression Modeling

Yonar

2022

Gazi University Journal of Science

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Section: Particle Swarm Optimizationmentioning

confidence: 99%

An Efficient Hybrid Algorithm with Particle Swarm Optimization and Nelder-Mead Algorithm for Parameter Estimation of Nonlinear Regression Modeling

Yonar

2022

Gazi University Journal of Science

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“…The result of CMA-ES is better than the other methods. To further test the proposed method, the calculation is carried out for different search spaces [38]. The results are listed in Table 4, which is average of ten calculations.…”

Section: Examplementioning

confidence: 99%

Maximum Likelihood Estimation for Three‐Parameter Weibull Distribution Using Evolutionary Strategy

Yang

Ren

2019

Mathematical Problems in Engineering

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The maximum likelihood estimation is a widely used approach to the parameter estimation. However, the conventional algorithm makes the estimation procedure of three-parameter Weibull distribution difficult. Therefore, this paper proposes an evolutionary strategy to explore the good solutions based on the maximum likelihood method. The maximizing process of likelihood function is converted to an optimization problem. The evolutionary algorithm is employed to obtain the optimal parameters for the likelihood function. Examples are presented to demonstrate the proposed method. The results show that the proposed method is suitable for the parameter estimation of the three-parameter Weibull distribution.

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“…For example, Sakin and Ay [2] studied the bending fatigue properties of glass fiber reinforced polyester composite plates and fitted the data with two-parameter Weibull distribution. Acitas et al [3] assumed that the strength data of glass fiber followed the Weibull distribution and estimated the parameters with maximum likelihood estimation (MLE). Zhu et al [4] developed the technology of making high strength refractory ceramic fibers using fly ash, and the mechanical properties of a series of fly ash fibers were assessed by the Weibull distribution.…”

Section: Introductionmentioning

confidence: 99%

“…Hidekazu et al [26] proposed an improved estimation method for MLE, which improved the estimation of Weibull shape parameter, scale parameter, and quantile. Acitas et al [3] used particle swarm optimization to solve the MLE of the Weibull distribution and applied the results to the strength data of glass fiber. Although the above literatures on parameter estimation and reliability analysis of the Weibull distribution are valuable, there is a lack of research on the parameter estimation method of strength data fitting the Weibull distribution (Datsiou and Overend [25]).…”

Section: Introductionmentioning

confidence: 99%

Parameter Estimation and Applications of the Weibull Distribution for Strength Data of Glass Fiber

Xie

Chiang

et al. 2021

Mathematical Problems in Engineering

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Glass fiber is a good substitute for metal materials. However, in the process of manufacturing, it is necessary to carry out sampling inspection on its tensile strength to infer its quality. According to previous literatures, the strength data can be well fitted by the Weibull distribution, while the poor parameter estimation method cannot obtain reliable analysis results. Therefore, a new parameter estimation method is proposed. Based on the simulation results, it is found that the proposed parameter estimation method outperforms the other competitors to obtain reliable estimates of the Weibull parameters. Finally, the proposed parameter estimation method is applied to two real data sets of glass fiber strength for illustration. The results of data analysis show that our proposed parameter estimation method is more suitable for these data sets than other estimation methods.

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A new approach for estimating the parameters of Weibull distribution via particle swarm optimization: An application to the strengths of glass fibre data

Cited by 51 publications

References 38 publications

An Efficient Hybrid Algorithm with Particle Swarm Optimization and Nelder-Mead Algorithm for Parameter Estimation of Nonlinear Regression Modeling

An Efficient Hybrid Algorithm with Particle Swarm Optimization and Nelder-Mead Algorithm for Parameter Estimation of Nonlinear Regression Modeling

Maximum Likelihood Estimation for Three‐Parameter Weibull Distribution Using Evolutionary Strategy

Parameter Estimation and Applications of the Weibull Distribution for Strength Data of Glass Fiber

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