一、五種算法簡介

1、蜣螂優化算法DBO

2、鯨魚優化算法WOA

3、灰狼優化算法GWO

4、粒子群優化算法PSO

5、遺傳算法GA

二、5種算法求解23個函數

（1）23個函數簡介

參考文獻：

[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代碼

from FunInfo import Get_Functions_details
from WOA import WOA
from GWO import GWO
from PSO import PSO
from GA import GA
from DBO import DBO
import matplotlib.pyplot as plt
from func_plot import func_plot
plt.rcParams['font.sans-serif']=['Microsoft YaHei']
#主程序
function_name =8 #測試函數1-23
SearchAgents_no = 50#種群大小
Max_iter = 100#迭代次數
lb,ub,dim,fobj=Get_Functions_details(function_name)#獲取問題信息
BestX1,BestF1,curve1 = WOA(SearchAgents_no, Max_iter,lb,ub,dim,fobj)#問題求解
BestX2,BestF2,curve2 = GWO(SearchAgents_no, Max_iter,lb,ub,dim,fobj)#問題求解
BestX3,BestF3,curve3 = PSO(SearchAgents_no, Max_iter,lb,ub,dim,fobj)#問題求解
BestX4,BestF4,curve4 = GA(SearchAgents_no, Max_iter,lb,ub,dim,fobj)#問題求解
BestX5,BestF5,curve5 = DBO(SearchAgents_no, Max_iter,lb,ub,dim,fobj)#問題求解
#畫函數圖
func_plot(lb,ub,dim,fobj,function_name)#畫函數圖
#畫收斂曲線圖
Labelstr=['WOA','GWO','PSO','GA','DBO']
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()

（3）部分結果

三、完整python代碼