聚类质心与指数递减方法改进的哈里斯鹰算法

Improved Harris Hawks Optimization Algorithm Based on Cluster Centroid and Exponential Decline Method

为了提高哈里斯鹰算法的寻优性能,提出KmHHO算法。首先,将所有种群看作一个聚类,用Kmeans算法计算聚类的质心,再用质心代替算法中的均值。然后,为了控制算法的探索和开发阶段,用指数递减的猎物逃逸能量,代替原算法中线性递减的猎物逃逸能量。最后,通过在23个benchmark函数上5个算法寻优性能的对比,验证KmHHO的改进效果,并利用Wilcoxon秩和检验,分析KmHHO与其他4个算法的差异性。实验结果表明,在23个benchmark函数中,KmHHO能够在14个benchmark函数上取得最优值,整体性能高于GWO、HHO和AO,但与DAHHO相当。

To promote optimization performance of Harris hawks optimization algorithm,KmHHO algorithm is proposed.Firstly,all populations as a cluster,the cluster centroid is calculated with Kmeans of Matlab,mean of HHO is replaced by cluster centroid.Then,to control the segments of exploration and development,linearly decreasing escape energy of prey is replaced with exponentially decreasing escape energy of prey.Finally,searching performance of five algorithms is compared on 23 benchmark functions,the improved effect of KmHHO is verified and Wilcoxon rank sum test is utilized to analyze the difference of KmHHO with other four optimization algorithms.The experimental results show that among the 23 benchmarks,KmHHO can achieve the optimal value on 14 benchmark functions,and its overall performance is higher than GWO,HHO and AO,but it’s equivalent to DAHHO.