本文实例为大家分享了python3单目标粒子群算法的具体代码，供大家参考，具体内容如下

关于PSO的基本知识......就说一下算法流程

1) 初始化粒子群；

随机设置各粒子的位置和速度，默认粒子的初始位置为粒子最优位置，并根据所有粒子最优位置，选取群体最优位置。

2) 判断是否达到迭代次数；

若没有达到，则跳转到步骤3）。否则，直接输出结果。

3) 更新所有粒子的位置和速度；

4) 计算各粒子的适应度值。

将粒子当前位置的适应度值与粒子最优位置的适应度值进行比较，决定是否更新粒子最优位置；将所有粒子最优位置的适应度值与群体最优位置的适应度值进行比较，决定是否更新群体最优位置。然后，跳转到步骤2）。

直接扔代码......(PS：1.参数动态调节；2.例子是二维的）

首先，是一些准备工作...

# Import libsimport numpy as npimport random as rdimport matplotlib.pyplot as plt # Constant definitionMIN_POS = [-5, -5] # Minimum position of the particleMAX_POS = [5, 5] # Maximum position of the particleMIN_SPD = [-0.5, -0.5] # Minimum speed of the particleMAX_SPD = [1, 1] # Maximum speed of the particleC1_MIN = 0C1_MAX = 1.5C2_MIN = 0C2_MAX = 1.5W_MAX = 1.4W_MIN = 0

然后是PSO类

# Class definitionclass PSO(): """ PSO class """ def __init__(self,iters=100,pcount=50,pdim=2,mode='min'): """ PSO initialization ------------------ """ self.w = None # Inertia factor self.c1 = None # Learning factor self.c2 = None # Learning factor self.iters = iters # Number of iterations self.pcount = pcount # Number of particles self.pdim = pdim # Particle dimension self.gbpos = np.array([0.0]*pdim) # Group optimal position self.mode = mode # The mode of PSO self.cur_pos = np.zeros((pcount, pdim)) # Current position of the particle self.cur_spd = np.zeros((pcount, pdim)) # Current speed of the particle self.bpos = np.zeros((pcount, pdim)) # The optimal position of the particle self.trace = [] # Record the function value of the optimal solution def init_particles(self): """ init_particles function ----------------------- """ # Generating particle swarm for i in range(self.pcount): for j in range(self.pdim): self.cur_pos[i,j] = rd.uniform(MIN_POS[j], MAX_POS[j]) self.cur_spd[i,j] = rd.uniform(MIN_SPD[j], MAX_SPD[j]) self.bpos[i,j] = self.cur_pos[i,j] # Initial group optimal position for i in range(self.pcount): if self.mode == 'min': if self.fitness(self.cur_pos[i]) < self.fitness(self.gbpos): gbpos = self.cur_pos[i] elif self.mode == 'max': if self.fitness(self.cur_pos[i]) > self.fitness(self.gbpos): gbpos = self.cur_pos[i] def fitness(self, x): """ fitness function ---------------- Parameter: x : """ # Objective function fitval = 5*np.cos(x[0]*x[1])+x[0]*x[1]+x[1]**3 # min # Retyrn value return fitval def adaptive(self, t, p, c1, c2, w): """ """ #w = 0.95 #0.9-1.2 if t == 0: c1 = 0 c2 = 0 w = 0.95 else: if self.mode == 'min': # c1 if self.fitness(self.cur_pos[p]) > self.fitness(self.bpos[p]): c1 = C1_MIN + (t/self.iters)*C1_MAX + np.random.uniform(0,0.1) elif self.fitness(self.cur_pos[p]) <= self.fitness(self.bpos[p]): c1 = c1 # c2 if self.fitness(self.bpos[p]) > self.fitness(self.gbpos): c2 = C2_MIN + (t/self.iters)*C2_MAX + np.random.uniform(0,0.1) elif self.fitness(self.bpos[p]) <= self.fitness(self.gbpos): c2 = c2 # w #c1 = C1_MAX - (C1_MAX-C1_MIN)*(t/self.iters) #c2 = C2_MIN + (C2_MAX-C2_MIN)*(t/self.iters) w = W_MAX - (W_MAX-W_MIN)*(t/self.iters) elif self.mode == 'max': pass return c1, c2, w def update(self, t): """ update function --------------- Note that : 1. Update particle position 2. Update particle speed 3. Update particle optimal position 4. Update group optimal position """ # Part1 : Traverse the particle swarm for i in range(self.pcount): # Dynamic parameters self.c1, self.c2, self.w = self.adaptive(t,i,self.c1,self.c2,self.w) # Calculate the speed after particle iteration # Update particle speed self.cur_spd[i] = self.w*self.cur_spd[i] \ +self.c1*rd.uniform(0,1)*(self.bpos[i]-self.cur_pos[i])\ +self.c2*rd.uniform(0,1)*(self.gbpos - self.cur_pos[i]) for n in range(self.pdim): if self.cur_spd[i,n] > MAX_SPD[n]: self.cur_spd[i,n] = MAX_SPD[n] elif self.cur_spd[i,n] < MIN_SPD[n]: self.cur_spd[i,n] = MIN_SPD[n] # Calculate the position after particle iteration # Update particle position self.cur_pos[i] = self.cur_pos[i] + self.cur_spd[i] for n in range(self.pdim): if self.cur_pos[i,n] > MAX_POS[n]: self.cur_pos[i,n] = MAX_POS[n] elif self.cur_pos[i,n] < MIN_POS[n]: self.cur_pos[i,n] = MIN_POS[n] # Part2 : Update particle optimal position for k in range(self.pcount): if self.mode == 'min': if self.fitness(self.cur_pos[k]) < self.fitness(self.bpos[k]): self.bpos[k] = self.cur_pos[k] elif self.mode == 'max': if self.fitness(self.cur_pos[k]) > self.fitness(self.bpos[k]): self.bpos[k] = self.cur_pos[k] # Part3 : Update group optimal position for k in range(self.pcount): if self.mode == 'min': if self.fitness(self.bpos[k]) < self.fitness(self.gbpos): self.gbpos = self.bpos[k] elif self.mode == 'max': if self.fitness(self.bpos[k]) > self.fitness(self.gbpos): self.gbpos = self.bpos[k] def run(self): """ run function ------------- """ # Initialize the particle swarm self.init_particles() # Iteration for t in range(self.iters): # Update all particle information self.update(t) # self.trace.append(self.fitness(self.gbpos))

然后是main...

def main(): """ main function """ for i in range(1): pso = PSO(iters=100,pcount=50,pdim=2, mode='min') pso.run() # print('='*40) print('= Optimal solution:') print('= x=', pso.gbpos[0]) print('= y=', pso.gbpos[1]) print('= Function value:') print('= f(x,y)=', pso.fitness(pso.gbpos)) #print(pso.w) print('='*40) # plt.plot(pso.trace, 'r') title = 'MIN: ' + str(pso.fitness(pso.gbpos)) plt.title(title) plt.xlabel("Number of iterations") plt.ylabel("Function values") plt.show() # input('= Press any key to exit...') print('='*40) exit() if __name__ == "__main__": main()

最后是计算结果，完美结束！！！

以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持。