一、五种算法简介
1、麻雀搜索算法SSA
2、鲸鱼优化算法WOA
3、灰狼优化算法GWO
4、粒子群优化算法PSO
5、遗传算法GA
二、5种算法求解23个函数
(1)23个函数简介
参考文献:
[1] Yao X, Liu Y, Lin G M. Evolutionary programming made faster[J]. IEEE transactions on evolutionary computation, 1999, 3(2):82-102.
(2)部分python代码
from FunInfo import Get_Functions_details from WOA import WOA from GWO import GWO from PSO import PSO from GA import GA from SSA import SSA import matplotlib.pyplot as plt from func_plot import func_plot plt.rcParams['font.sans-serif']=['Microsoft YaHei'] #主程序 function_name =7 #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 = WOA(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 = PSO(SearchAgents_no, Max_iter,lb,ub,dim,fobj)#问题求解 BestX4,BestF4,curve4 = GA(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 #画函数图 func_plot(lb,ub,dim,fobj,function_name)#画函数图 #画收敛曲线图 Labelstr=['WOA','GWO','PSO','GA','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() #