import numpy as np

 import ObjFunction

 class FAIndividual:

     '''

     individual of firefly algorithm

     '''

     def __init__(self,  vardim, bound):

         '''

         vardim: dimension of variables

         bound: boundaries of variables

         '''

         self.vardim = vardim

         self.bound = bound

         self.fitness = 0.

         self.trials = 0

     def generate(self):

         '''

         generate a random chromsome for firefly algorithm

         '''

         len = self.vardim

         rnd = np.random.random(size=len)

         self.chrom = np.zeros(len)

         for i in xrange(0, len):

             self.chrom[i] = self.bound[0, i] + \

                 (self.bound[1, i] - self.bound[0, i]) * rnd[i]

     def calculateFitness(self):

         '''

         calculate the fitness of the chromsome

         '''

         self.fitness = ObjFunction.GrieFunc(

             self.vardim, self.chrom, self.bound)

 import numpy as np

 from FAIndividual import FAIndividual

 import random

 import copy

 import matplotlib.pyplot as plt

 class FireflyAlgorithm:

     '''

     The class for firefly algorithm

     '''

     def __init__(self, sizepop, vardim, bound, MAXGEN, params):

         '''

         sizepop: population sizepop

         vardim: dimension of variables

         bound: boundaries of variables

         MAXGEN: termination condition

         param: algorithm required parameters, it is a list which is consisting of [beta0, gamma, alpha]

         '''

         self.sizepop = sizepop

         self.MAXGEN = MAXGEN

         self.vardim = vardim

         self.bound = bound

         self.population = []

         self.fitness = np.zeros((self.sizepop, 1))

         self.trace = np.zeros((self.MAXGEN, 2))

         self.params = params

     def initialize(self):

         '''

         initialize the population

         '''

         for i in xrange(0, self.sizepop):

             ind = FAIndividual(self.vardim, self.bound)

             ind.generate()

             self.population.append(ind)

     def evaluate(self):

         '''

         evaluation of the population fitnesses

         '''

         for i in xrange(0, self.sizepop):

             self.population[i].calculateFitness()

             self.fitness[i] = self.population[i].fitness

     def solve(self):

         '''

         evolution process of firefly algorithm

         '''

         self.t = 0

         self.initialize()

         self.evaluate()

         best = np.max(self.fitness)

         bestIndex = np.argmax(self.fitness)

         self.best = copy.deepcopy(self.population[bestIndex])

         self.avefitness = np.mean(self.fitness)

         self.trace[self.t, 0] = (1 - self.best.fitness) / self.best.fitness

         self.trace[self.t, 1] = (1 - self.avefitness) / self.avefitness

         print("Generation %d: optimal function value is: %f; average function value is %f" % (

             self.t, self.trace[self.t, 0], self.trace[self.t, 1]))

         while (self.t < self.MAXGEN - 1):

             self.t += 1

             self.move()

             self.evaluate()

             best = np.max(self.fitness)

             bestIndex = np.argmax(self.fitness)

             if best > self.best.fitness:

                 self.best = copy.deepcopy(self.population[bestIndex])

             self.avefitness = np.mean(self.fitness)

             self.trace[self.t, 0] = (1 - self.best.fitness) / self.best.fitness

             self.trace[self.t, 1] = (1 - self.avefitness) / self.avefitness

             print("Generation %d: optimal function value is: %f; average function value is %f" % (

                 self.t, self.trace[self.t, 0], self.trace[self.t, 1]))

         print("Optimal function value is: %f; " %

               self.trace[self.t, 0])

         print "Optimal solution is:"

         print self.best.chrom

         self.printResult()

     def move(self):

         '''

         move the a firefly to another brighter firefly

         '''

         for i in xrange(0, self.sizepop):

             for j in xrange(0, self.sizepop):

                 if self.fitness[j] > self.fitness[i]:

                     r = np.linalg.norm(

                         self.population[i].chrom - self.population[j].chrom)

                     beta = self.params[0] * \

                         np.exp(-1 * self.params[1] * (r ** 2))

                     # beta = 1 / (1 + self.params[1] * r)

                     # print beta

                     self.population[i].chrom += beta * (self.population[j].chrom - self.population[

                         i].chrom) + self.params[2] * np.random.uniform(low=-1, high=1, size=self.vardim)

                     for k in xrange(0, self.vardim):

                         if self.population[i].chrom[k] < self.bound[0, k]:

                             self.population[i].chrom[k] = self.bound[0, k]

                         if self.population[i].chrom[k] > self.bound[1, k]:

                             self.population[i].chrom[k] = self.bound[1, k]

                     self.population[i].calculateFitness()

                     self.fitness[i] = self.population[i].fitness

     def printResult(self):

         '''

         plot the result of the firefly algorithm

         '''

         x = np.arange(0, self.MAXGEN)

         y1 = self.trace[:, 0]

         y2 = self.trace[:, 1]

         plt.plot(x, y1, 'r', label='optimal value')

         plt.plot(x, y2, 'g', label='average value')

         plt.xlabel("Iteration")

         plt.ylabel("function value")

         plt.title("Firefly Algorithm for function optimization")

         plt.legend()

         plt.show()

 if __name__ == "__main__":

     bound = np.tile([[-600], [600]], 25)

     fa = FA(60, 25, bound, 200, [1.0, 0.000001, 0.6])

     fa.solve()