摘要
提出了一种具有自适应搜索能力的快速收敛遗传算法 .在计算过程中 ,设计变量的搜索范围依据每代自变量的数学期望和方差自动进行调整 ,并且通过引入进化策略中的自适应高斯变异算子 ,对变异算子进行改进 ,加速了算法的收敛性 .为了验证算法的可行性和鲁棒性 ,对一个高维多峰函数的极小值搜索问题进行了求解 ,并将算法进一步应用于离心叶轮的形状优化问题 .计算结果表明 ,该算法克服了传统遗传算法中设计区间的给定具有一定盲目性的缺陷 ,在收敛性和鲁棒性方面均优于传统的实数编码遗传算法 .
In conventional real genetic algorithm, a minimum and a maximum value for each design variable must be set before genetic operators are given. However, information about the minimum and maximum values is not known, and the design space is set blindfold and stochastically. A new type of real genetic algorithm named adaptive real range search genetic algorithm is proposed, in which a range of real numbers will move adaptively in each generation by using the mean value and the standard deviation of the previous generations. In addition, an improved Gauss mutation operator of evolutionary strategy is used in order to speed up convergence. In order to verify algorithmic rationality and validity, the improved genetic algorithm is applied to compute a multi-modal function and study shape optimization of a centrifugal impeller. The results show that this method excels the conventional real genetic algorithm in the convergence and robust.
出处
《西安交通大学学报》
EI
CAS
CSCD
北大核心
2002年第3期226-229,256,共5页
Journal of Xi'an Jiaotong University
基金
教育部高等学校博士点专项科研基金资助项目 (970 6 982 0 )
关键词
自适应搜索
改进遗传算法
高斯变异算子
Binary codes
Convergence of numerical methods
Impellers
Mathematical operators
Optimization
Robustness (control systems)
