摘要
利用Karush-Kuhn-Tucker条件,将下层为凸规划的非线性双层规划转化为一个单层规划问题.为了提高遗传算法求解该问题的效率,利用对线性不等式约束添加松弛项和计算非线性约束边界点的方法,给出了一种新的约束处理方法;通过构造一个辅助线性模型降低了搜索空间的维数;结合算法产生的最优个体,设计了一个有助于改善个体适应度的杂交算子.
In terms of the Karush-Kuhn-Tucker conditions of convex programming, a special nonlinear bilevel programming problem, whose follower-level problem is a convex programming, is transformed into an equivalent single-level programming problem. To solve the transformed problem effectively by using the genetic algorithm, firstly, a new constraint-handling scheme is proposed by adding slack terms to linear inequality constraints and by solving boundary points on nonlinear constraints; secondly, a linear model is constructed to decrease the dimensions of the search space; finally, a new crossover operator is designed, based on the best individuals generated by the algorithm, and it is helpful to improve the fitness values of crossover offspring.
出处
《西安电子科技大学学报》
EI
CAS
CSCD
北大核心
2007年第1期101-105,共5页
Journal of Xidian University
基金
国家自然科学基金资助(60374063)
关键词
非线性双层规划
凸规划
约束处理
全局最优解
遗传算法
nonlinear bilevel programming
convex programming
constraint handling
global optimization
genetic algorithm
