一、五种算法简介
1、人工蜂群算法 (Artificial Bee Colony Algorithm, ABC)
2、粒子群优化算法PSO
3、鸡群优化算法CSO
4、鱼鹰优化算法OOA
5、蜣螂优化算法DBO
二、5种算法求解CEC2013
(1)CEC2013简介
参考文献:
[1] Liang J J , Qu B Y , Suganthan P N , et al. Problem Definitions and Evaluation Criteria for the CEC 2013 Special Session on Real-Parameter Optimization. 2013.
(2)部分python代码
from CEC2013.cec2013 import * import numpy as np from ABC import ABC from PSO import PSO from OOA import OOA from CSO import CSO from DBO import DBO import matplotlib.pyplot as plt plt.rcParams['font.sans-serif']=['Microsoft YaHei'] #主程序 #主程序 function_name =7 #测试函数1-28 SearchAgents_no = 50#种群大小 Max_iter = 100#迭代次数 dim=10#维度 10/30/50/100 lb=-100*np.ones(dim)#下限 ub=100*np.ones(dim)#上限 cec_functions = cec2013(dim,function_name) fobj=cec_functions.func#目标函数 BestX1,BestF1,curve1 = ABC(SearchAgents_no, Max_iter,lb,ub,dim,fobj)#问题求解 BestX2,BestF2,curve2 = PSO(SearchAgents_no, Max_iter,lb,ub,dim,fobj)#问题求解 BestX3,BestF3,curve3 = OOA(SearchAgents_no, Max_iter,lb,ub,dim,fobj)#问题求解 BestX4,BestF4,curve4 = CSO(SearchAgents_no, Max_iter,lb,ub,dim,fobj)#问题求解 BestX5,BestF5,curve5 = DBO(SearchAgents_no, Max_iter,lb,ub,dim,fobj)#问题求解 #画收敛曲线图 Labelstr=['ABC','PSO','OOA','CSO','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("cec2013-F"+str(function_name)) plt.legend() plt.savefig(str(function_name)+'.png') plt.show() #