March 2025
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12 Reads
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1 Citation
Swarm and Evolutionary Computation
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Publications (57)
![a Discrepancy comparison and b Pie chart in the case of n=L\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$n = L$$\end{document}](https://www.researchgate.net/publication/382593008/figure/fig4/AS:11431281284631567@1729389176291/a-Discrepancy-comparison-and-b-Pie-chart-in-the-case-of-nLdocumentclass12ptminimal_Q320.jpg)
![Discrepancy comparison: aL=3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$L = 3$$\end{document}, bL=4\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$L = 4$$\end{document}, and cL=5\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$L = 5$$\end{document} and Pie chart: dL=3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$L = 3$$\end{document}, eL=4\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$L = 4$$\end{document}, and fL=5\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$L = 5$$\end{document}.](https://www.researchgate.net/publication/382593008/figure/fig5/AS:11431281284676044@1729389176493/Discrepancy-comparison-aL3documentclass12ptminimal-usepackageamsmath_Q320.jpg)
July 2024
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24 Reads
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6 Citations
Soft Computing
Generating uniform design tables (UDTs) is the first step to experimenting efficiently and effectively, and is also one of the most critical steps. Thus, the construction of uniform design tables has received much attention over the past decades. This paper presents a new algorithm for constructing uniform design tables: restart discrete dynamical evolutionary algorithm (RDDE). This algorithm is based on a well-designed dynamical evolutionary algorithm and utilizes discrete rounding technology to convert continuous variables into discrete variables. Considering the optimization of UDT is a multi-objective optimization problem, RDDE uses Friedman rank to select the optimal solution with better comprehensive comparison ranking. RDDE also utilizes a simulated annealing-based restart technology to select control parameters, thereby increasing the algorithm's ability to jump out of local optima. Comparisons with state-of-the-art UDTs and two practical engineering examples are presented to verify the uniformity of the design table constructed by RDDE. Numerical results indicate that RDDE can indeed construct UDTs with excellent uniformity at different levels, factors, and runs. Especially, RDDE can flexibly construct UDTs with unequal intervals of factors that cannot be directly processed by other designs of experiment.
June 2024
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33 Reads
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6 Citations
International Journal of Mechanical Sciences
January 2024
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12 Reads
January 2024
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12 Reads
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11 Citations
Annals of Nuclear Energy
September 2023
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14 Reads
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14 Citations
Annals of Nuclear Energy
May 2023
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31 Reads
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4 Citations
Acta Mechanica Sinica
An algorithm based on the Paterson-Stockmeyer (PS) scheme and filtering technology is developed to compute the large matrix functions in dynamic problems accurately and efficiently. With the assistance of analysis on truncation error and error caused by filtering, the error growth law during the computation is studied, based on which an adaptive filtering threshold is proposed to help the proposed algorithm more efficiently achieve similar accuracy as the original PS scheme. Numerical examples, including 30 random matrices with different bandwidths, 10 adjacency matrices in the complex network dynamic problems, and a trampoline vibration problem, are given to verify the efficiency and accuracy of the proposed algorithm. Numerical results suggest that the proposed algorithm can achieve good accuracy and efficiency in computing the matrix function in the considered dynamic problems.
May 2023
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8 Reads
Mathematics and Computers in Simulation
March 2023
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44 Reads
Compared with random sampling, low-discrepancy sampling is more effective in covering the search space. However, the existing research cannot definitely state whether the impact of a low-discrepancy sample on particle swarm optimization (PSO) is positive or negative. Using Niderreiter's theorem, this study completes an error analysis of PSO, which reveals that the error bound of PSO at each iteration depends on the dispersion of the sample set in an expanded dimensional space. Based on this error analysis, an acceleration technique for PSO-type algorithms is proposed with low-discrepancy sampling in the expanded dimensional space. The acceleration technique can generate a low-discrepancy sample set with a smaller dispersion, compared with a random sampling, in the expanded dimensional space; it also reduces the error at each iteration, and hence improves the convergence speed. The acceleration technique is combined with the standard PSO and the comprehensive learning particle swarm optimization, and the performance of the improved algorithm is compared with the original algorithm. The experimental results show that the two improved algorithms have significantly faster convergence speed under the same accuracy requirement.
March 2023
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34 Reads
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10 Citations
Journal of Computational Science
Citations (43)
... These methods can be broadly divided into gradient-based techniques and metaheuristic algorithms. While gradient-based methods rely on mathematical derivatives, metaheuristic algorithms are favored for their flexibility, derivative-free nature, and ease of implementation [8,9]. Among optimization techniques. ...
- Citing Article
March 2025
Swarm and Evolutionary Computation
... Therefore, it is of crucial importance to consider the uncertainties in engineering design (Ditlevsen O 1996;Huang et al. 2023Huang et al. , 2024. However, due to the limited storage capacity of sensors or the high cost of observations, it is difficult to obtain sufficient samples for many engineering problems, which leads to scarce samples (Fröderberg and Thelandersson 2015;Jung et al. 2024;Zhao et al. 2024). As a result, it is impossible to accurately evaluate their probability information through probability density functions by using conventional probabilistic methods (Acar et al. 2021;Dai et al. 2023;Karuna and Manohar 2016). ...
- Citing Article
- Publisher preview available
July 2024
Soft Computing
... The approach outlined in this paper is utilized for static parameter identification. Future endeavors will consider extending the application of this precise and efficient method to the dynamic parameter identification problem present in nonlinear dynamical systems [47][48][49][50]. The issues that need to be addressed include the proper modeling of the dynamic system, and modeling the mathematical relationship between the measured variables and the system parameters. ...
- Citing Article
June 2024
International Journal of Mechanical Sciences
... Over the past few decades, optimization problems have become increasingly prevalent across various industrial and scientific domains, including mechanical engineering [1], nuclear energy [2], vehicle engineering [3], reliability analysis [4], aerospace engineering [5], and topology optimization [6] The growing complexity of real-world problems, such as nonlinear optimal control, text clustering, DNA sequence compression, and distribution network design, has intensified the demand for efficient optimization algorithms. As a result, research on optimization techniques holds significant practical value and broad applicability [7]. ...
- Citing Article
January 2024
Annals of Nuclear Energy
... We need a stochastic analysis method that is computationally simple and does not require a large number of samples. Wu et al. [22,23] proposed the stochastic perturbation collocation (SPC) method based on random perturbation theory. This method constructs the expressions for the mean and variance of random quantities by selecting specific collocation points. ...
- Citing Article
September 2023
Annals of Nuclear Energy
... For all sample points to reach a stationary state, an artificial damping force is applied to each sample, thus Eq. (12) can be represented as the following common dynamical equation [59]: ...
- Citing Article
May 2023
Acta Mechanica Sinica
... Constructing efficient and high-precision time integration algorithms to predict the process of structural displacement, velocity, and other responses changing over time has always been one of the core research directions in the field of computational mechanics [3,4]. In this direction, a variety of time integration algorithms have been developed, such as the Newmark method [5], the symplectic method [6,7], the precise integration method [8][9][10], and so on. However, these methods mainly aim to solving deterministic structural dynamical response problems. ...
- Citing Article
February 2023
Acta Mechanica Solida Sinica
... where ξ denotes the measurement errors. From Eq (2), it can be seen that due to the influence of measurement errors, the objective function for each parameter identification process may vary, leading to different identified parameters. In other words, the parameters e are a function of the measurement errors ξ : ...
- Citing Article
March 2023
Journal of Computational Science
... This is the prerequisite for the validity of Eqs (5) and (6). The detailed derivation of Eqs (5) and (6) can be referenced in [38]. From Eqs (5) and (6), it is known that the SPC method listed here has an accuracy of fifth-order, which is much higher than the second-order accuracy of SPM. ...
- Citing Article
December 2022
Annals of Nuclear Energy
... The numerical characteristics of the stationary interval processes constructed by the proposed methods and the corresponding calculation times are listed in Table 8, respectively, where the construction time means the time that is spent for modeling the interval processes. After obtaining the covariance matrix and the interval median with the MVSIP-SCK and MRSIP-SCK methods, we use interval K-L expansion method combined with low-discrepancy sampling (Ni et al. 2020;Wu et al. , 2022 to obtain 100,000 groups of artificial road surface excitation loads. ...
- Citing Article
August 2022
Swarm and Evolutionary Computation