(1)红狐优化算法(Red fox optimization,RFO)
(2)灰狼优化算法(Grey Wolf Optimizer,GWO)
(3)蜣螂优化算法(Dung beetle optimizer,DBO)
(4)哈里斯鹰优化算法(Harris Hawks Optimization,HHO)
(5)麻雀搜索算法(sparrow search algorithm,SSA)
23个基本函数介绍
测试集:23组基本测试函数简介及图像(提供python代码)_IT猿手的博客-CSDN博客
部分代码:
from FunInfo import Get_Functions_details from RFO import RFO from GWO import GWO from DBO import DBO from HHO import HHO from SSA import SSA import matplotlib.pyplot as plt plt.rcParams['font.sans-serif']=['Microsoft YaHei'] #主程序 function_name =14 #测试函数1-23 SearchAgents_no = 50#种群大小 Max_iter = 100#迭代次数 lb,ub,dim,fobj=Get_Functions_details(function_name)#获取问题信息 BestX1,BestF1,curve1 = RFO(SearchAgents_no, Max_iter,lb,ub,dim,fobj)#问题求解 BestX2,BestF2,curve2 = GWO(SearchAgents_no, Max_iter,lb,ub,dim,fobj)#问题求解 BestX3,BestF3,curve3 = DBO(SearchAgents_no, Max_iter,lb,ub,dim,fobj)#问题求解 BestX4,BestF4,curve4 = HHO(SearchAgents_no, Max_iter,lb,ub,dim,fobj)#问题求解 BestX5,BestF5,curve5 = SSA(SearchAgents_no, Max_iter,lb,ub,dim,fobj)#问题求解 #画收敛曲线图 Labelstr=['RFO','GWO','DBO','HHO','SSA'] Colorstr=['r','g','b','k','c'] if BestF1>0: ? ? plt.semilogy(curve1,color=Colorstr[0],linewidth=2,label=Labelstr[0]) ? ? plt.semilogy(curve2,color=Colorstr[1],linewidth=2,label=Labelstr[1]) ? ? plt.semilogy(curve3,color=Colorstr[2],linewidth=2,label=Labelstr[2]) ? ? plt.semilogy(curve4,color=Colorstr[3],linewidth=2,label=Labelstr[3]) ? ? plt.semilogy(curve5,color=Colorstr[4],linewidth=2,label=Labelstr[4]) else: ? ? plt.plot(curve1,color=Colorstr[0],linewidth=2,label=Labelstr[0]) ? ? plt.plot(curve2,color=Colorstr[1],linewidth=2,label=Labelstr[1]) ? ? plt.plot(curve3,color=Colorstr[2],linewidth=2,label=Labelstr[2]) ? ? plt.plot(curve4,color=Colorstr[3],linewidth=2,label=Labelstr[3]) ? ? plt.plot(curve5,color=Colorstr[4],linewidth=2,label=Labelstr[4]) plt.xlabel("Iteration") plt.ylabel("Fitness") plt.xlim(0,Max_iter) plt.title("F"+str(function_name)) plt.legend() plt.savefig(str(function_name)+'.png') plt.show()
部分结果: