Structural reliability assessment based on particles swarm optimization

Elegbede, Charles

doi:10.1016/j.strusafe.2004.10.003

Cited by 125 publications

(66 citation statements)

References 13 publications

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“…DE has a good optimizing performance and has been employed to solve numerical optimization problems [12][13]. PSO having gained popularity shows significant performance in solving many problems and has been applied to wide applications [14][15][16][17][18].…”

Section: A Experiments Sets and Benchmark Functionsmentioning

confidence: 99%

A hybrid firefly algorithm

Zhu²,

Zhang

2017

2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)

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Section: A Experiments Sets and Benchmark Functionsmentioning

confidence: 99%

A hybrid firefly algorithm

Zhu²,

Zhang

2017

2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)

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“…Three main transformation methods to solve Eq. (2) have been summarized by Elegbede (2005). The solution u * of Eq.…”

Section: Structural Reliability and Psomentioning

confidence: 99%

“…For different optimization problems, the population size may be different. According to Eberhart and Shi (2000) and Elegbede (2005), the population size is selected to be 70, and the learning factors c 1 and c 2 are all set as 2.05 in this study.…”

Section: Structural Reliability and Psomentioning

confidence: 99%

“…PSO has been used to solve a range of optimization problems, such as neural network training and function minimization (Eberhart and Hu, 1999;Engelbrecht and Ismail, 1999;Shi and Eberhart, 1999;van den Bergh and Engelbrecht, 2000). Elegbede (2005) used PSO to calculate the Hasofer-Lind reliability index which is the minimum distance from the origin to the limitstate function in standard normal space (a constrained optimization problem). The study indicated that PSO can be considered as an additional efficient algorithm to those existing in the literature based on gradient methods which do not ensure the detection of the global optimum.…”

Section: Introductionmentioning

confidence: 99%

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A structural reliability-based sensitivity analysis method using particles swarm optimization: relative convergence rate

Lan

Peng

et al. 2016

J. Zhejiang Univ. Sci. A

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This paper proposes a novel reliability-based sensitivity analysis (SA) method, namely relative convergence rate of random variables using particles swarm optimization (PSO). The convergence rate of a random variable during the optimum evolution process reflects the sensitivity of the objective function with respect to the random variables. An optimized group strategy is proposed to consider the fluctuation of the convergence rate of a variable during the optimum process. The coefficient of variation (COV) for candidate particles and the relative convergence rate of a random variable can be calculated using the particles in the optimized group. The smaller the COV for candidate particles, i.e., the larger the relative convergence rate, the more sensitive the objective function with respect to the variable. Three examples are available for the application of this method, and the results indicate that the sensitivity of the reliability index with respect to the variable obtained using the PSO technique and gradient of limit-state function is the same in the quantitative sense.

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“…PSO has gained popularity lately and has been applied to wide applications in different fields [15][16][17][18][19]. Since the frame of this algorithm is relatively simple and the operation speed is very fast, PSO has also been applied in hydrological parameter optimization [20,21].…”

Section: Introductionmentioning

confidence: 99%

Improved particle swarm algorithm for hydrological parameter optimization

Jiang

Liu

Huang

et al. 2010

Applied Mathematics and Computation

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a b s t r a c tIn this paper, a new method named MSSE-PSO (master-slave swarms shuffling evolution algorithm based on particle swarm optimization) is proposed. Firstly, a population of points is sampled randomly from the feasible space, and then partitioned into several sub-swarms (one master swarm and other slave swarms). Each slave swarm independently executes PSO or its variants, including the update of particles' position and velocity. For the master swarm, the particles enhance themselves based on the social knowledge of master swarm and that of slave swarms. At periodic stage in the evolution, the master swarm and the whole slave swarms are forced to mix, and points are then reassigned to several subswarms to ensure the share of information. The process is repeated until a user-defined stopping criterion is reached. The tests of numerical simulation and the case study on hydrological model show that MSSE-PSO remarkably improves the accuracy of calibration, reduces the time of computation and enhances the performance of stability. Therefore, it is an effective and efficient global optimization method.

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Structural reliability assessment based on particles swarm optimization

Cited by 125 publications

References 13 publications

A hybrid firefly algorithm

A hybrid firefly algorithm

A structural reliability-based sensitivity analysis method using particles swarm optimization: relative convergence rate

Improved particle swarm algorithm for hydrological parameter optimization

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