标签：Dim 背包 random 算法 适应度 xSize xbest np 微粒
import random
import math
import matplotlib.pyplot as plt
import numpy as np
import time
def init(b_=700,xSize_=200,iteration_=1000,c1_=0.5,c2_=0.5,w_=0.8):
global a,c,b,Dim,xSize,iteration,c1,c2,w,A,C,x,v,xbest,fxbest,gbest,fgbest
'''
a = []
b = []
for i in range(100):
a.append(random.randint(1,99))
b.append(random.randint(1,99))
#自定义初始数据
'''
a = [90, 33, 94, 69, 77, 91, 39, 74, 24, 34, 14, 89, 98, 37, 32, 45, 15, 98, 40, 16, 17, 4, 3, 5, 94, 3, 64, 47, 85, 9, 6, 39, 44, 67, 33, 59, 17, 16, 55, 95, 69, 88, 91, 28, 66, 54, 85, 82, 24, 17, 30, 66, 96, 8, 74, 20, 84, 35, 53, 19, 25, 64, 98, 93, 86, 24, 30, 68, 56, 37, 6, 98, 48, 76, 61, 9, 29, 76, 55, 41, 93, 19, 56, 85, 20, 84, 12, 64, 94, 29, 26, 93, 83, 72, 76, 86, 4, 99, 29, 4]
c = [68, 20, 125, 85, 113, 109, 19, 93, 17, 19, 18, 79, 92, 39, 45, 50, 12, 77, 53, 16, 8, 2, 2, 7, 47, 4, 67, 9, 62, 6, 8, 51, 61, 93, 33, 35, 14, 15, 34, 136, 87, 72, 121, 28, 84, 54, 110, 44, 15, 23, 36, 95, 77, 11, 74, 14, 75, 50, 63, 26, 34, 56, 67, 77, 73, 34, 41, 59, 84, 44, 7, 112, 70, 65, 54, 6, 20, 92, 56, 21, 125, 24, 83, 113, 14, 99, 18, 49, 70, 15, 34, 88, 105, 48, 61, 128, 3, 110, 19, 4]
b = b_ #背包容量
#初始化种群
Dim = len(a) #维度
xSize = xSize_ #种群数
iteration = iteration_ #迭代次数
c1 = c1_
c2 = c2_ #加速因子
w = w_ #定义惯性因子
A = np.array([a]*xSize) #将a扩展成种群数*维度的矩阵
C = np.array([c]*xSize) #将c扩展为种群数*维度的矩阵
x = np.random.randint(0,2,(xSize,Dim)) #随机生成一个种群数*维度的矩阵
v = np.random.rand(xSize,Dim) #随机生成一个种群数*维度的速度矩阵
xbest = np.zeros((xSize,Dim)) #单个粒子的初始最佳位置
fxbest = np.zeros((xSize,1)) #xbest的适应度
gbest = np.zeros((1,Dim)) #全局最优解
fgbest = 0 #全局最优解的适应度
def solve():
#寻找粒子群最优位置和单个粒子
global x,fgbest,v
fx = np.sum(np.multiply(C,x), axis=1) # 粒子适应度，即背包内物品的价格
sx = np.sum(np.multiply(A,x), axis=1) # 限制函数，即背包内物品的体积
#print(sx)
for i in range(xSize):
if list(sx)[i] > b:
fx[i] = 0
for i in range(xSize):
if fxbest[i] < fx[i]: # 如果当前粒子适应度大于最佳适应度，替代最佳适应度fxbest，替代最佳粒子xbest
fxbest[i] = fx[i]
xbest[i] = x[i] # 替换矩阵第i行
if fgbest <= max(fxbest):
fgbest = max(fxbest)
g = list(fxbest).index(fgbest)
gbest = xbest[g] #当存在粒子的最佳适应度fxbext(i)大于种群最佳适应度fgbext(i)时，替代
for i in range(xSize):
if (x[i]==gbest).all():
x[i] = np.random.randint(0,2,(1,Dim)) #随机生成一个种群数*维度的矩阵
R1 = np.random.rand(xSize,Dim)
R2 = np.random.rand(xSize,Dim) # xSize行Dim列的0~1随机矩阵
'''速度v和位置x的迭代公式'''
v = v * w + c1 * np.multiply(R1,xbest-x) + c2 * np.multiply(2,(np.array([gbest]*xSize)-x))
x = x + v
for i in range(xSize): #更新粒子群的位置
for j in range(Dim):
if x[i][j] < 0.5: # 粒子的位置只有（0,1）两种状态R
x[i][j] = 0
else:
x[i][j] = 1
if __name__ == "__main__":
k = 0 # 功能选项
if(k==1):
'''在不同种群规模下的算法性能表现'''
tmp = []
y = []
for scale in range(50,200,20):
tmp.append(scale)
print(scale)
init(xSize_=scale,iteration_=1500)
for i in range(iteration):
solve()
y.append(fgbest[0])
plt.plot(tmp,y)
plt.xlabel('population scale')
plt.ylabel('value')
plt.title('the effect of population scale')
elif(k==2):
'''在不同惯性因子下的算法性能表现'''
tmp = []
y = []
for w in range(4,9):
w = w / 10
tmp.append(w)
print(w)
init(w_=w,iteration_=1500)
for i in range(iteration):
solve()
y.append(fgbest[0])
plt.plot(tmp,y)
plt.xlabel('inertia factor')
plt.ylabel('value')
plt.title('the effect of inertia factor')
elif(k==3):
'''在不同加速因子下的算法性能表现'''
tmp = []
y = []
for c in range(1,20,2):
c = c / 10
tmp.append(c)
print(c)
init(c1_=c,c2_=c,iteration_=1500)
for i in range(iteration):
solve()
y.append(fgbest[0])
plt.plot(tmp,y)
plt.xlabel('acceleration factor')
plt.ylabel('value')
plt.title('the effect of acceletation factor')
elif(k==4):
'''在不同背包容量下的算法最佳迭代次数'''
tmp = []
y = []
for capacity in range(500,3501,500):
tmp.append(capacity)
best = []
print(capacity)
init(b_=capacity,iteration_=5000)
for i in range(iteration):
solve()
best.append(fgbest[0])
print(best)
for i in range(len(best)):
if(best[len(best)-i-1] != best[len(best)-i-2]):
print(4)
y.append(len(best)-i)
break
plt.xlabel('capacity')
plt.ylabel('epochs')
plt.plot(tmp,y)
elif(k==5):
'''禁忌搜索算法与微粒群算法的性能比较'''
from TSA import TSA_bag
i = 500
tmp = [x for x in range(i)]
pso = []
init(iteration_=i)
for i in range(iteration):
solve()
pso.append(fgbest[0])
plt.plot(tmp,pso,color='red',label='PSO')
plt.plot(tmp,TSA_bag(interation=i),color='blue',label='TSA')
plt.xlabel('interation')
plt.ylabel('value')
plt.title('the comparison between PSA and TSA')
elif(k==6):
'''在不同背包容量下，禁忌搜索算法与微粒群算法的性能比较'''
#import TSA
tmp = []
pso = []
tsa = []
for i in range(500,3501,250):
import TSA
TSA_tmp = TSA.main(capacity_=i,interation=300)
tmp.append(i)
print(i)
init(b_=i,iteration_=1000)
for i in range(iteration):
solve()
pso.append(fgbest[0])
tsa.append(TSA_tmp)
plt.plot(tmp,pso,color='red',label='PSO')
plt.plot(tmp,tsa,color='blue',label='TSA')
plt.xlabel('capacity')
plt.ylabel('value')
plt.title('the comparison between PSA and TSA')
plt.legend()
plt.show()
'''有参考MATLAB代码。自己仿照写了整个python程序、6个探究、并且增加了惯性权重、删除了Vmax等变量限制。'''
标签：Dim,
背包,
random,
算法,
适应度,
xSize,
xbest,
np,
微粒
From： https://www.cnblogs.com/chengjunkai/p/16745979.html