In order to obtain reliable operating conditions, uncertainties must be taken into account in the mathematical modeling of systems and processes. An uncertainty is measured by the probability of failure of the process, which can be tackled by inverse reliability analysis. This analysis guarantees the achievement of the probabilistic constraints at a specified level. Here, we propose a new methodology for obtaining optimum operating conditions, considering certain probabilistic constraints. The method uses a second order approximation for the calculation of an adaptive step length, in a technique based on steepest descent method, to optimize the performance function. The efficiency of the proposed technique is evaluated in some benchmark problems and in an engineering problem, showing that it outperforms other recent methodologies in convergence capability and stability, while being robust in the choice of control parameters.

为了获得可靠的运行条件，在系统和过程的数学建模中必须考虑不确定性。通过过程失败的概率来衡量不确定性，可以通过逆可靠性分析来解决。这种分析保证了在特定级别上实现概率约束。在这里，考虑到某些概率约束，我们提出了一种获得最佳运行条件的新方法。在基于最速下降法的技术中，该方法使用二阶近似来计算自适应步长，以优化性能函数。在某些基准问题和工程问题中评估了所提出技术的效率，