python3实现单目标粒子群算法

# 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

# 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))

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()