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 libs
import numpy as np
import random as rd
import matplotlib.pyplot as plt

# Constant definition
MIN_POS = [-5, -5]         # Minimum position of the particle
MAX_POS = [5, 5]          # Maximum position of the particle
MIN_SPD = [-0.5, -0.5]        # Minimum speed of the particle
MAX_SPD = [1, 1]          # Maximum speed of the particle
C1_MIN = 0
C1_MAX = 1.5
C2_MIN = 0
C2_MAX = 1.5
W_MAX = 1.4
W_MIN = 0

然后是PSO类

# Class definition
class 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python博客。