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基于拟蒙特卡罗方法的进化算法搜索鲁棒最优解的性能提高研究

, PP. 201-209

Keywords: 进化算法,鲁棒最优解,拟蒙特卡罗方法,有效目标函数

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Abstract:

鲁棒最优解在工程应用中具有十分重要的意义,它是进化计算的重要研究内容,也是研究难点。进化算法搜索鲁棒最优解时,通常使用蒙特卡罗积分(MCI)近似估计有效目标函数(EOF),但由于现有的原始蒙特卡罗方法(C-MC)近似精度不高,导致进化算法搜索鲁棒最优解的性能较差。文中提出用拟蒙特卡罗方法(Q-MC)估计有效目标函数。通过大量的数值实验,结果表明,与C-MC相比,文中所引入的Q-MC方法——SQRT序列、SOBOL序列和Korobov点阵能更精确估计EOF,进而较大提高进化算法搜索鲁棒最优解的性能。

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