一、红狐优化算法

红狐优化算法（Red fox optimization，RFO）由Dawid Połap和 Marcin Woźniak于2021年提出，该算法模拟了红狐的狩猎行为，具有收敛速度快，寻优精度高等优势。

参考文献： Poap D , Woniak M . Red fox optimization algorithm[J]. Expert Systems with Applications, 2021, 166(10):114107.

二、RFO求解cec2017

cec2017简介

参考文献：
Awad, N. H., Ali, M. Z., Liang, J. J., Qu, B. Y., & Suganthan, P. N. (2016). “Problem definitions and evaluation criteria for the CEC2017 special session and competition on single objective real-parameter numerical optimization,” Technical Report. Nanyang Technological University, Singapore.

部分代码

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from RFO import RFO
import matplotlib.pyplot as plt
import numpy as np
import cec2017.functions as functions
#主程序
function_name =1 #测试函数 1-30
SearchAgents_no = 50#种群大小
Max_iter = 100#最大迭代次数
dim=10;#维度只能是 10/30/50/100
lb = -100*np.ones(dim)#下界
ub = 100*np.ones(dim)#上界
fobj= functions.all_functions[function_name-1]
BestX,BestF,curve = RFO(SearchAgents_no, Max_iter,lb,ub,dim,fobj)#问题求解


#画收敛曲线图
if BestF>0:
    plt.semilogy(curve,color='r',linewidth=2,label='RFO')
else:
    plt.plot(curve,color='r',linewidth=2,label='RFO')
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()
print('\nThe best solution is:\n'+str(BestX))
print('\nThe best optimal value of the objective funciton is:\n'+str(BestF))

部分结果

三、参考代码

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