刘化龙;胡钋;

• 1:武汉大学电气工程学院

摘要(Abstract)：

针对基本遗传算法(genetic algorithm,GA)局部寻优能力较差和易出现早熟收敛现象,提出了一种改进的遗传算法,即序列二次规划-遗传算法(sequential quadratic programminggenetic algorithm,SQP-GA)。该混合优化算法SQP-GA在基本GA中引入序列二次规划(sequential quadratic programming,SQP)算法,经典算法SQP作为一个元算子有效地提高了基本GA的局部搜索能力,并克服了早熟收敛现象。函数仿真测试结果表明,SQP-GA混合优化算法在收敛速度和寻优精度上都优于基本GA,表明所提出的算法的有效性。同时,利用提出的SQP-GA求解变压器局部放电超声波定位问题,并将其定位结果与GA和非智能算法的定位结果进行比较;算例结果表明,基于SQP-GA的变压器局部放电超声定位法能有效地防止结果陷入局部最优,该方法的定位效果理想。

关键词(KeyWords)： 遗传算法;序列二次规划-遗传算法;变压器;局部放电;超声波定位;非智能算法

Abstract:

Keywords:

基金项目(Foundation):

作者(Author): 刘化龙;胡钋;

Email:

DOI: 10.13335/j.1000-3673.pst.2015.01.020

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