A new type of solving method for reliability model parameter optimization

Most of solving method for reliability growth model parameters is to structure maximum likelihood function and extremum on the likelihood function. When the maximum likelihood method is adopted for the evaluation and optimization of parameter, because of the effect of iterative initial value, the classical one is not easy to converge to the global optimal solution. Evolutionary programming (EP) algorithm used in this paper, its feature is to establish the fitness function, find great value point to determine optimization model of the parameters optimal solution, and it is not necessary to find extreme value and estimate initial value of the optimization variable to obtain global approximate optimal solution. In order to better ensure global optimal solution, to further ensure the accuracy of the equations, a parallel operation and retain the best individual and other methods are used in evolutionary programming algorithm. The new method for solving the optimization parameters can achieve a better balance in the solving efficiency and convergence performance and better combined optimization methods with maximum likelihood estimation method. The validity and correctness of the optimization method is verified in the last of the paper through the experimental data in the reliability growth process of a solid rocket motor.