摘要
提出了一种有效求解约束函数优化问题的新型演化算法,该方法能合理地处理优化设计中混合离散变量的取值问题。该方法是在郭涛算法的基础上,通过构造动罚函数,引入精英保存策略,增加父体选择压力加速算法收敛,构造了精英多父体杂交优化算法,开发了混合离散变量优化的精英多父体杂交优化算法程序DEMPCOA1.0。机械优化设计实例表明,该算法对优化设计问题的特性无特殊要求,具有较好的适应性,而且程序运行可靠,全局收敛能力强。
A new approach to handle constrained function optimization problems by using evolutionary algorithms is presented.The value adopting problem of hybrid discrete variable in optimization design is dealing with reasonably by the method.On the basis of Guo Tao algorithm,a elite multi-parent crossover evolutionary optimization algorithm is constructed by introducing the elite preservation strategy,constructing dynamic penalty function and enhancing the selection pressure of parents in the process of algorithm convergence.The elite multi-parent crossover evolutionary optimization algorithm program DEMPCOA1.0 with hybrid discrete variables is developed.The living example of mechanical optimization design show that this algorithm has no special requirement on the characteristic of optimal design problem.The universal adaptability is fairly good,operation of program is reliable and the ability of overall convergence is strong.
出处
《机械传动》
CSCD
北大核心
2010年第10期40-42,47,共4页
Journal of Mechanical Transmission
基金
湖南省"十一五"重点建设学科(机械设计及理论)(湘教通2006180)
国家自然科学基金(51075144)
湖南省科技计划项目(2009GK3158)
关键词
演化算法
函数优化
精英保存
混合离散变量
非线性约束优化
Evolutionary algorithm Function optimization Elite preservation Hybrid discrete variables Nonlinear constrained optimization
