摘要
对浮点编码遗传算法加以改进,并与DFP变尺度算法相结合,经加速循环,构建新型混合加速遗传算法(以下简称NHAGA);协同求解具有变量边界约束的非凸、高度非线性的复杂函数最优化问题。算例测试表明,该法兼顾了改进浮点编码遗传算法全局搜索能力和DFP算法快速局部搜索能力的优点,成功搜索全局最优点的概率较高,是一种求解非凸、高度非线性全局优化问题的有效智能算法。
Based on the DFP method and improvable real-code genetic algorithm, a novel hybrid accelerating genetic algorithm (NHAGA), which is used for optimizing complex nonlinear functions, is established by setting the DFP method in real-code genetic algorithm improvably. Numerical results show that this method keeps advantages of both the DFP method and improvable real-code genetic algorithm, and has a higher probability for successfully finding the global optimization solution. It is thus an efficient intellect algorithm for solving nonconvex and highly nonlinear global optimization problem.
出处
《工程数学学报》
CSCD
北大核心
2005年第3期518-524,共7页
Chinese Journal of Engineering Mathematics
基金
国家自然科学基金重点项目(59838300)湖南省自然科学基金(03jjy6020).
关键词
非线性函数
DFP变尺度算法
混合遗传算法
优化
the nonlinear function
DFP
hybrid accelerating genetic algorithm
optimization
