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Chuang Xiong

Bio: Chuang Xiong is an academic researcher from Beihang University. The author has contributed to research in topics: Interval (mathematics) & Reliability (statistics). The author has an hindex of 7, co-authored 7 publications receiving 257 citations.

Papers
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Journal ArticleDOI
TL;DR: Results indicate that the presented iterative dimension-wise method has a superiority in uncertainty propagation problems of multidisciplinary issues.

99 citations

Journal ArticleDOI
TL;DR: IDDM seeks the minimum and maximum points of the uncertain variables dimension-by-dimension and updates the nominal value of other uncertain variables through an iterative process and is efficient for structural interval uncertainty analysis, especially for problems with multidimensional uncertain variables.

59 citations

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TL;DR: A sequential multidisciplinary design optimization and reliability analysis method under non-probabilistic theory is developed to decouple the reliability analysis from the optimization.

48 citations

Journal ArticleDOI
TL;DR: A new definition of the hybrid time-variant reliability measurement is provided for the vibration control systems and the related solution details are further expounded.

46 citations

Journal ArticleDOI
TL;DR: In the framework of SMO_NTRA, the deterministic MDO and non-probabilistic time-dependent reliability analysis are executed in a sequential manner, and the computationally expensive double level optimization problem can be avoided and the efficiency can be greatly improved.

30 citations


Cited by
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Journal ArticleDOI
TL;DR: In this method, Kriging metamodel is employed to replace the true performance function, and it is smartly updated based on the samples in the first and last levels of subset simulation (SS) to achieve the smart update.
Abstract: This paper proposes an efficient Kriging-based subset simulation (KSS) method for hybrid reliability analysis under random and interval variables (HRA-RI) with small failure probability. In this method, Kriging metamodel is employed to replace the true performance function, and it is smartly updated based on the samples in the first and last levels of subset simulation (SS). To achieve the smart update, a new update strategy is developed to search out samples located around the projection outlines on the limit-state surface. Meanwhile, the number of samples in each level of SS is adaptively adjusted according to the coefficients of variation of estimated failure probabilities. Besides, to quantify the Kriging metamodel uncertainty in the estimation of the upper and lower bounds of the small failure probability, two uncertainty functions are defined and the corresponding termination conditions are developed to control Kriging update. The performance of KSS is tested by four examples. Results indicate that KSS is accurate and efficient for HRA-RI with small failure probability.

113 citations

Journal ArticleDOI
TL;DR: A framework of dynamic interval multiobjective cooperative co-evolutionary optimization based on the interval similarity is presented in this paper to handle DI-MOPs and it is revealed that the proposed algorithm is very competitive on most optimization instances.
Abstract: Dynamic interval multiobjective optimization problems (DI-MOPs) are very common in real-world applications. However, there are few evolutionary algorithms (EAs) that are suitable for tackling DI-MOPs up to date. A framework of dynamic interval multiobjective cooperative co-evolutionary optimization based on the interval similarity is presented in this paper to handle DI-MOPs. In the framework, a strategy for decomposing decision variables is first proposed, through which all the decision variables are divided into two groups according to the interval similarity between each decision variable and interval parameters. Following that, two subpopulations are utilized to cooperatively optimize decision variables in the two groups. Furthermore, two response strategies, i.e., a strategy based on the change intensity and a random mutation strategy, are employed to rapidly track the changing Pareto front of the optimization problem. The proposed algorithm is applied to eight benchmark optimization instances as well as a multiperiod portfolio selection problem and compared with five state-of-the-art EAs. The experimental results reveal that the proposed algorithm is very competitive on most optimization instances.

107 citations

Journal ArticleDOI
TL;DR: A non-probability based method to calculate time-dependent reliability is introduced to estimate the safety of a vibration active control system of based on PID controller performance to avoid the disadvantages of traditional probabilistic methods.

102 citations

Journal ArticleDOI
TL;DR: Results indicate that the presented iterative dimension-wise method has a superiority in uncertainty propagation problems of multidisciplinary issues.

99 citations

Journal ArticleDOI
TL;DR: This paper incorporates several local searches into an existing IMOEA, and proposes a memetic algorithm (MA) to tackle IMOPs, and experimental results demonstrate the applicability and effectiveness of the proposed MA.
Abstract: One of the most important and widely faced optimization problems in real applications is the interval multiobjective optimization problems (IMOPs). The state-of-the-art evolutionary algorithms (EAs) for IMOPs (IMOEAs) need a great deal of objective function evaluations to find a final Pareto front with good convergence and even distribution. Further, the final Pareto front is of great uncertainty. In this paper, we incorporate several local searches into an existing IMOEA, and propose a memetic algorithm (MA) to tackle IMOPs. At the start, the existing IMOEA is utilized to explore the entire decision space; then, the increment of the hypervolume is employed to develop an activation strategy for every local search procedure; finally, the local search procedure is conducted by constituting its initial population, whose center is an individual with a small uncertainty and a big contribution to the hypervolume, taking the contribution of an individual to the hypervolume as its fitness function, and performing the conventional genetic operators. The proposed MA is empirically evaluated on ten benchmark IMOPs as well as an uncertain solar desalination optimization problem and compared with three state-of-the-art algorithms with no local search procedure. The experimental results demonstrate the applicability and effectiveness of the proposed MA.

83 citations