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grasscodergrasscoder
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6-2
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‎.settings/org.eclipse.core.resources.prefs

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Original file line numberDiff line numberDiff line change
@@ -3,6 +3,7 @@ encoding/CAPalloction.py=utf-8
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encoding/Drawpic.py=gbk
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encoding/SINR.py=utf-8
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encoding/brest.py=utf-8
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encoding/chanAllocate.py=utf-8
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encoding/coverage.py=utf-8
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encoding/flask_email.py=utf-8
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encoding/pract.py=utf-8

‎pso.py

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@@ -4,29 +4,13 @@
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import numpy as np
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import matplotlib.pyplot as plt
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from CAPalloction import readFile,classifyUser,getPower,channelAllocate,turnInToParticle
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class Bird(object):##定义一个鸟类（粒子类）
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def __init__(self, speed, position, fit, lBestPosition, lBestFit):
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'''
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初始化的信息：speed粒子的速率，position：粒子的位置，fit适应度，lBestPostion粒子经理的最佳位置，
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lBestFit粒子的最佳是硬度值
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'''
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self.speed = speed
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self.position = position
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self.fit = fit
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self.lBestFit = lBestFit
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self.lBestPosition = lBestPosition
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#----------------------PSO参数设置---------------------------------
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class PSO():
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def __init__(self,pN,dim,max_iter):
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self.w = 0.8
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self.c1 = 2
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self.c2 = 2
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self.c1 = 1.4961#2
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self.c2 = 1.4961#2
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self.r1= 0.6
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self.r2=0.3
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self.channelnum = 64
@@ -35,15 +19,19 @@ def __init__(self,pN,dim,max_iter):
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self.max_iter = max_iter #迭代次数
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self.X = np.zeros((self.pN,self.dim)) #所有粒子的位置和速度
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self.V = np.zeros((self.pN,self.dim))
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self.pbest = np.zeros((self.pN,self.dim)) #个体经历的最佳位置和全局最佳位置
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self.gbest = np.zeros((1,self.dim))
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self.pbest = np.zeros((self.pN,self.dim)) #个体经历的最佳位置
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self.gbest = np.zeros((1,self.dim)) #全局最佳位置
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self.p_fit = np.zeros(self.pN) #每个个体的历史最佳适应值
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self.fit = 1e10 #全局最佳适应值
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self.filename = ['user.txt','bs.txt']
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self.UserX,self.UserY,self.BSX,self.BSY = readFile(*self.filename)
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self.BSCover = classifyUser(100,self.UserX,self.UserY,self.BSX,self.BSY)
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self.BSchanAllocate=[[-1 for i in xrange(self.channelnum)] for j in xrange(len(self.BSCover))]#初始化信道分配
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#---------------------目标函数Sphere函数-----------------------------
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def function(self,x): #x是列表
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'''
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首要解决的问题是 目标函数的的表达式
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由于粒子由是不同信道的功率等级（比例）表示的，所以
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'''
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sum = 0
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length = len(x)
@@ -54,40 +42,31 @@ def function(self,x): #x是列表
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#---------------------初始化种群----------------------------------
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def init_Population(self):
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filename = ['user.txt','bs.txt']
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UserX,UserY,BSX,BSY = readFile(*filename)
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BSCover = classifyUser(100,UserX,UserY,BSX,BSY)
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BSchanAllocate=[[-1 for i in xrange(self.channelnum)] for j in xrange(len(BSCover))]#初始化信道分配
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####将宏基站的坐标加入到基站的坐标列表中
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BSX = BSX+[0.0]
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BSY = BSY+[0.0]
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self.BSX = self.BSX+[0.0]
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self.BSY = self.BSY+[0.0]
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for i in xrange(5):
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BSchanAllocate = channelAllocate(BSCover,BSchanAllocate,BSX,BSY)
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self.BSchanAllocate = channelAllocate(self.BSCover,self.BSchanAllocate,self.BSX,self.BSY)
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for i in xrange(self.pN):
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self.X[i] = turnInToParticle(getPower(BSchanAllocate))#初始化粒子位置
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self.X[i] = turnInToParticle(getPower(self.BSchanAllocate))#初始化粒子位置
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for j in range(self.dim):
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self.V[i][j] = random.uniform(0,1)
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self.pbest[i] = self.X[i]
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tmp = self.function(self.X[i])#目标函数
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self.p_fit[i] = tmp
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if(tmp < self.fit):
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self.fit = tmp
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self.V[i][j] = random.uniform(0,1) #初始化粒子的速度
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self.pbest[i] = self.X[i] ##每个粒子的最佳位置
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tmp = self.function(self.X[i])#调用目标函数，计算当前粒子的适应值，目标函数是处理每一个粒子的，在这个表达式中显而易见
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self.p_fit[i] = tmp ##每个粒子最佳适应值
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if(tmp < self.fit): #判断小于全局最佳适应值，将当前粒子的最佳适应值赋值给全局最佳适应值
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self.fit = tmp #
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self.gbest = self.X[i]
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# for i in range(self.pN):
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# for j in range(self.dim):
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# self.X[i][j] = random.uniform(0,1) #初始化种群的位置
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# self.V[i][j] = random.uniform(0,1) #初始化种群的速度
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# self.pbest[i] = self.X[i]
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# tmp = self.function(self.X[i])
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# self.p_fit[i] = tmp
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# if(tmp < self.fit):
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# self.fit = tmp
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# self.gbest = self.X[i]
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#
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#--------------------------粒子的惯性权重-----------------------------
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def inertia_Weight(self,t):
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"""保证惯性权重开始:0.9全局搜索能力强，最后为0.1:局部搜索能力强"""
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k = (0.9-0.1)/(1-self.max_iter)
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b = (0.9*self.max_iter-0.1)/(self.max_iter-1)
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y = k*t + b
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return y
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#----------------------更新粒子位置----------------------------------
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def iterator(self):
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fitness = []
@@ -97,16 +76,18 @@ def iterator(self):
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if(temp<self.p_fit[i]): #更新个体最优
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self.p_fit[i] = temp
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self.pbest[i] = self.X[i]
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if(self.p_fit[i] < self.fit): #更新全局最优
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if(self.p_fit[i] < self.fit):#更新全局最优
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self.gbest = self.X[i]
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self.fit = self.p_fit[i]
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self.fit = self.p_fit[i]
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self.w = self.inertia_Weight(t)##调用类内部的函数,改变w的值，收敛速度明显加快
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for i in range(self.pN):
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self.V[i] = self.w*self.V[i] + self.c1*self.r1*(self.pbest[i] - self.X[i])+ self.c2*self.r2*(self.gbest - self.X[i])
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self.X[i] = self.X[i] + self.V[i]
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fitness.append(self.fit)
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print(self.fit) #输出最优值
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return fitness
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#----------------------------------粒子群类构造结束-------------------------------------------------
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if __name__=="__main__":
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#----------------------程序执行-----------------------
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max_iter = 200

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