Implementation of Teaching Learning Based Optimization

# python implementation of Teaching learning based optimization (TLBO)
# minimizing rastrigin and sphere function

import random
import math    # cos() for Rastrigin
import copy    # array-copying convenience
import sys     # max float


#-------fitness functions---------

# rastrigin function
def fitness_rastrigin(position):
  fitness_value = 0.0
  for i in range(len(position)):
    xi = position[i]
    fitness_value += (xi * xi) - (10 * math.cos(2 * math.pi * xi)) + 10
  return fitness_value

#sphere function
def fitness_sphere(position):
    fitness_value = 0.0
    for i in range(len(position)):
        xi = position[i]
        fitness_value += (xi*xi);
    return fitness_value;
#-------------------------

#Student class
class Student:
  def __init__(self, fitness, dim, minx, maxx, seed):
    self.rnd = random.Random(seed)

    # a list of size dim 
    # with 0.0 as value of all the elements
    self.position = [0.0 for i in range(dim)]

    # loop dim times and randomly select value of decision var
    # value should be in between minx and maxx
    for i in range(dim):
      self.position[i] = ((maxx - minx) *
        self.rnd.random() + minx)

    # compute the fitness of student
    self.fitness = fitness(self.position)


# Teaching learning based optimization
def tlbo(fitness, max_iter, n, dim, minx, maxx):
  rnd = random.Random(0)

  # create n random students
  classroom = [Student(fitness, dim, minx, maxx, i) for i in range(n)] 

  # compute the value of best_position and best_fitness in the classroom
  Xbest = [0.0 for i in range(dim)]
  Fbest = sys.float_info.max        

  for i in range(n): # check each Student
    if classroom[i].fitness < Fbest:
      Fbest = classroom[i].fitness
      Xbest = copy.copy(classroom[i].position) 

  # main loop of tlbo
  Iter = 0
  while Iter < max_iter:
    
    # after every 10 iterations 
    # print iteration number and best fitness value so far
    if Iter % 10 == 0 and Iter > 1:
      print("Iter = " + str(Iter) + " best fitness = %.3f" % Fbest)

    # for each student of classroom
    for i in range(n): 

      ### Teaching phase of ith student

      # compute the mean of all the students in the class
      Xmean = [0.0 for i in range(dim)]
      for k in range(n):
          for j in range(dim):
              Xmean[j]+= classroom[k].position[j]
      
      for j in range(dim):
          Xmean[j]/= n;
      
      # initialize new solution
      Xnew = [0.0 for i in range(dim)]

      # teaching factor (TF)
      # either 1 or 2 ( randomly chosen)
      TF = random.randint(1, 3)

      # best student of the class is teacher
      Xteacher = Xbest

      # compute new solution 
      for j in range(dim):
          Xnew[j] = classroom[i].position[j] + rnd.random()*(Xteacher[j] - TF*Xmean[j])
      
      # if Xnew < minx OR Xnew > maxx
      # then clip it 
      for j in range(dim):
          Xnew[j] = max(Xnew[j], minx)
          Xnew[j] = min(Xnew[j], maxx)
      
      # compute fitness of new solution
      fnew = fitness(Xnew)

      # if new solution is better than old 
      # replace old with new solution
      if(fnew < classroom[i].fitness):
          classroom[i].position = Xnew
          classroom[i].fitness = fnew
       
      # update best student
      if(fnew < Fbest):
          Fbest = fnew
          Xbest = Xnew



      ### learning phase of ith student

      # randomly choose a solution from classroom
      # chosen solution should not be ith student
      p = random.randint(0, n-1)
      while(p==i):
          p = random.randint(0, n-1)
      
      # partner solution
      Xpartner = classroom[p]

      Xnew = [0.0 for i in range(dim)]
      if(classroom[i].fitness < Xpartner.fitness):
          for j in range(dim):
              Xnew[j] = classroom[i].position[j] + rnd.random()*(classroom[i].position[j] - Xpartner.position[j])
      else:
          for j in range(dim):
              Xnew[j] = classroom[i].position[j] - rnd.random()*(classroom[i].position[j] - Xpartner.position[j])

      # if Xnew < minx OR Xnew > maxx
      # then clip it 
      for j in range(dim):
          Xnew[j] = max(Xnew[j], minx)
          Xnew[j] = min(Xnew[j], maxx)
      
      # compute fitness of new solution
      fnew = fitness(Xnew)

      # if new solution is better than old 
      # replace old with new solution
      if(fnew < classroom[i].fitness):
          classroom[i].position = Xnew
          classroom[i].fitness = fnew
       
      # update best student
      if(fnew < Fbest):
          Fbest = fnew
          Xbest = Xnew

    Iter += 1
  # end-while

  # return best student from classroom
  return Xbest
# end pso


#----------------------------
# Driver code for rastrigin function

print("\nBegin  teaching learning based optimization on rastrigin function\n")
dim = 3
fitness = fitness_rastrigin


print("Goal is to minimize Rastrigin's function in " + str(dim) + " variables")
print("Function has known min = 0.0 at (", end="")
for i in range(dim-1):
  print("0, ", end="")
print("0)")

num_particles = 50
max_iter = 100

print("Setting num_particles = " + str(num_particles))
print("Setting max_iter    = " + str(max_iter))
print("\nStarting TLBO algorithm\n")



best_position = tlbo(fitness, max_iter, num_particles, dim, -10.0, 10.0)

print("\nTLBO completed\n")
print("\nBest Student found:")
print(["%.6f"%best_position[k] for k in range(dim)])
fitness_value = fitness(best_position)
print("fitness of best Student = %.6f" % fitness_value)

print("\nEnd TLBO for rastrigin function\n")


print()
print()


# Driver code for Sphere function 
print("\nBegin teaching learning based optimization on sphere function\n")
dim = 3
fitness = fitness_sphere


print("Goal is to minimize sphere function in " + str(dim) + " variables")
print("Function has known min = 0.0 at (", end="")
for i in range(dim-1):
  print("0, ", end="")
print("0)")

num_particles = 50
max_iter = 100

print("Setting num_particles = " + str(num_particles))
print("Setting max_iter    = " + str(max_iter))
print("\nStarting TLBO algorithm\n")



best_position = tlbo(fitness, max_iter, num_particles, dim, -10.0, 10.0)

print("\nTLBO completed\n")
print("\nBest Student found:")
print(["%.6f"%best_position[k] for k in range(dim)])
fitness_value = fitness(best_position)
print("fitness of best Student = %.6f" % fitness_value)

print("\nEnd TLBO for sphere function\n")