python3实现单目标粒子群算法

# 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

# 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))

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()