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Abstract: This paper proposes a novel reliability-based sensitivity analysis (SA) method, namely relative convergence rate of random variables using particles swarm optimization (). 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 technique and gradient of limit-state function is the same in the quantitative sense.

基于粒子群优化算法的结构可靠度敏感性分析方法：相对收敛率

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