ining N stochastic design variables and K stochastic parameters,f(X,W)repre- sents the objective specification which gives an indication of the quality of the de- sign,and f'(X,W)(i=1,2,..,m)represent design specifications which are linear or nonlinear stochastic functions,E represents expected value of stochastic specifications and P represents the probabilitiy that satisfies stochastic spe- cifications;a (j=m+1,m+2,.,m)are the specific probabilistic values which the stochastic constraints must satisfy.In the process of optimization, the expected values of stochastic variables are chosen to make iterations.That is mainly due to the expected values of stochastic variables are similar to the nominal values in traditional design methods.Also the stochastic optimization criteria such as min.Var..}and min.P(.}can be adopted according to the re- quirements of various designs.So it becomes more complicated to build the models when dealing with stochastic optimal design problems.As usual,only when all stochastic variables and parameters are normal distribution and the design specifications are simple functions,the stochastic optimization can be slo- ved by the use of the Chance Technique). In general cases,it must determine how the probabilistic distribution of each x varies as its mean varies,and at every iterative step in a numerical search,it is necessary to make a probabilistic analysis to obtain the mean of f (X,W)and the joint probability of failure associated with const:ained function. 1 General Principles of Algorithm The optimum of problem (1)may be described as follows: Dp and Ds(X)represent stochastic feasible region of problem (1)and a neighborhood of X. Supposing theX·∈Ds(X)cD,c(2,T,P),for allX∈Ds(X),if there exists fo(X,W)<f(X,W),then X is a constrained local stochastic optimum;and if for all XEDC(Q,T,P),there exists o(X.,W)f(X,W), then X.is a contrained global stochastic optimum. Several arbitrary assumptions are presented before the following discussions. The expected value of f (X,W)has been used in the optimization,and the distribution form of each stochastic variable dose not vary as the mean varies. In the execution of optimization,some characteristics of stochastic specfications are tried to be approximated step by step in order to save the solving time. A complete algorithm for stochastic optimization has to include two parts, one is the probabilitic analysis for the stochastic variables and stochastic design specifications,another is the search strategy of stochastic variables.In many 453， ” ， 平 ， ‘ ， 牙 ， ，… ， 切 五 ， 。 。 ’ ， ， ， 一 ， 五 。 ， 。 呈 一 。 。 ， ， ， ’ ， 五 ， ， ， 班 ， 。 尸 。 任 。 · 二 ， 压 口 ， ， ， 任 。 ’ ， “ ‘ ，平 “ ，牙 ， ‘ 任 二 口 ， ， ， 。 ， ，详 二 。 ，详 ， ， 。 ， 。 ， 亏 ， ， ，