模拟退火算法是一种用于解决连续、有序离散和多模态优化问题的随机优化技术。
SA is a stochastic optimization technique that has been used to solve continuous, order discrete and muti-modal optimization problems.
该模型是带有离散变量和连续变量的混合优化设计模型。
The model is concerned to a hybrid optimization design problem with disperse and continuous variables.
本文给出了随机型优化问题的分类，并综述了离散参数和连续参数随机型优化问题中典型的求解算法。
This paper gives a simple classification of stochastic optimization problems, and then summarizes important algorithms for stochastic optimization in discrete and continuous parameters.
采用多目标规划的思想，将离散变量优化和连续变量优化结合起来，较好解决了离散变量优化设计时的难点。
Joined up optimization of continuous variable and discrete variable, the difficult was solved by using the idea of multi-objections.
对离散变量的析架尺寸优化问题，本文提出了采用多目标规划的思想、将离散变量优化和连续变量优化结合起来的求解方法。
In sizing optimization of truss with discrete variables, joined up optimization of continuous variable and discrete variable, the idea of multi-objections is used.
本文将其引入到舯剖面横向构件的优化设计牛，以解决连续和离散混合设计变量的优化问题。
It is introduced to solve the mixed variable problems in optimizing the cross-sectional transverse members.
对离散事件动态系统研究的仿真优化方法的最新进展进行了综述。根据仿真输入参数，分为连续参数方法和离散参数方法两种进行讨论。
A brief survey of the recent literature on discrete event simulation optimization is presented, and it separated into discrete and continuous input parameters.
对于函数优化这个问题，根据目标函数定义域的性质，可以分为离散函数最优化和连续函数最优化。
For function optimization problems, according to the nature of the objective function domain, can be divided into discrete function optimization and continuous function optimization.
通过对连续变量无约束优化、连续变量约束优化和离散变量约束优化等典型优化问题的计算分析 ，将三种惩罚函数方法进行了比较 ，指出了它们的特点及选用原则。
The selection of the key parameters and genetic operators is discussed. The calculation results of three computational examples are given. The characteristic of the three methods is pointed.
通过对连续变量无约束优化、连续变量约束优化和离散变量约束优化等典型优化问题的计算分析 ，将三种惩罚函数方法进行了比较 ，指出了它们的特点及选用原则。
The selection of the key parameters and genetic operators is discussed. The calculation results of three computational examples are given. The characteristic of the three methods is pointed.
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