基于自适应遗传算法的负荷特性分类

Load characteristics classification based on adaptive genetic algorithm

提出了运用一种改进的遗传算法对电力负荷特性进行分类的新方法。通过对样本进行遗传操作,求出适应度最高的个体,解码得到最优聚类中心,再根据样本与各中心距离进行划分,从而得到负荷样本的最优分类结果,用获得分类的聚类中心对所属类别样本进行拟合以检验分类效果。改进后的遗传算法的交叉概率和变异概率随进化过程自适应变化,在保证遗传算法良好的全局性和随机性的同时,避免了早熟收敛和收敛过慢。实际算例表明,用这种改进遗传算法对电力负荷特性进行分类,能够有效避免初始条件对分类结果的过度影响,取得了良好的分类效果。

A new method based on improved genetic algorithm is presented for load characteristics classification. The best individual which is of the highest fitness can be obtained by genetic manipulation on samples, and the individual is decoded to get the best cluster center, then the optimal classification is obtained by dividing samples based on the distance of the samples and the cluster centers, and finally the samples are fitted with the cluster centers of respective categories to test the classification accuracy. While ensuring the overall performance and randomness of adaptive genetic algorithm, the adaptive changing of the crossover probability and mutation probability with the process of evolution proposed in this paper can avoid the premature convergence and slow convergence which may appear in traditional genetic algorithm. Practical examples show that it can avoid the excessive impact of the in- itial conditions on the classification results and achieves desired classification results when classifying load charac- teristics with adaptive genetic algorithm.