Global Optimization by Generalized Random Tunneling Algorithm (4th Report Application to the Nonlinear Optimum Design Problem of the Mixed Design Variables)
Global Optimization by Generalized Random Tunneling Algorithm (4th Report Application to the Nonlinear Optimum Design Problem of the Mixed Design Variables)
复制标题
广义随机隧道算法的全局优化（第四报告在混合设计变量的非线性优化设计问题中的应用）
DOI：
10.1299/jcst.2.258
复制
发表时间：
2006
期刊：
影响因子：
--
通讯作者：
K. Yamazaki
中科院分区：
文献类型：
--
作者：
S. Kitayama;K. Yamazaki
本文提出了一种基于改进的广义随机隧道算法（MGRTA）的离散和连续设计变量的全局或准最优解的求解方法。通过将离散设计变量作为罚函数处理，构造了增广目标函数。因此，所有的设计变量可以被视为连续的设计变量。增广的目标函数变得非凸，并有许多局部极小值。也就是说，寻找最优的离散设计变量转化为寻找全局最优的这个增广的目标函数。然后将MGRTA应用于此增强的目标函数，受行为和侧约束。对于离散设计变量的罚函数，本文还提出了新的罚参数更新方案。所提出的惩罚参数更新方案利用离散设计变量的惩罚函数值的信息。利用MGRTA的特点，得到了一些优化结果。通过典型的基准问题，所提出的方法的有效性进行了检查。
This paper presents a method to obtain the global or quasi-optimum for the discrete and continuous design variables, based on the Modified Generalized Random Tunneling Algorithm (MGRTA). By handling the discrete design variables as penalty function, the augmented objective function is constructed. As a result, all design variables can be treated as the continuous design variables. The augmented objective function becomes non-convex, and has many local minima. That is, finding optimum of discrete design variables is transformed into finding global optimum of this augmented objective function. Then the MGRTA is applied to this augmented objective function, subject to the behavior and side constraints. We also propose the new update scheme of penalty parameter for the penalty function of discrete design variables in this paper. The proposed update scheme of penalty parameter utilizes the information of the penalty function value of discrete design variables. By utilizing the characteristics of MGRTA, some optima are obtained. The validity of the proposed method is examined through typical benchmark problems.