摘要
引入了自适应的惩罚因子,将约束问题转化为无约束问题.通过遗传算法求得无约束问题的可行解,再将此解作为约束变尺度法的初始可行点,由约束变尺度法得到精度较高的解.数值实验表明该混合算法比单纯使用遗传算法效率高,而且在多数情况下能得到全局最优解.
The authors introduce a sort of novel adaptive penalty gene, transform the constrained problem into unconstrained problems. An solution is given for this unconstrained problem with genetic algorithm, and then it is used as initial values for the constrained variable metric method to get precise solution. The numerical experiments illustrate that this hybrid genetic algorithm is more efficient than the genetic algorithm, and at most situations globally optimal solution can be gotten.
出处
《重庆大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2006年第6期78-81,共4页
Journal of Chongqing University
关键词
非线性规划
惩罚函数
遗传算法
约束变尺度法
nonlinear programming problems
penelty function
genetic algorithm
the constrained variable metric method
