nsgaii.py

#!/usr/bin/env python
# encoding: utf-8

import numpy as np
from nd_sort import nd_sort
from crowding_distance import crowding_distance
from tournament import tournament
from environment_selection import environment_selection
from GLOBAL import Global

from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import time

Global = Global(M=3)


class nsgaii(object):
    """
    NSGA-II algorithm
    """

    def __init__(self, decs=None, ite=100, eva=100 * 500):
        self.decs = decs
        self.ite = ite
        self.eva = eva

    def run(self):
        start = time.clock()
        if self.decs is None:
            population = Global.initialize()
        else:
            population = Global.individual(self.decs)

        front_no, max_front = nd_sort(population[1], np.inf)
        crowd_dis = crowding_distance(population[1], front_no)
        evaluation = self.eva
        while self.eva >= 0:
            fit = np.vstack((front_no, crowd_dis)).T
            mating_pool = tournament(2, Global.N, fit)
            pop_dec, pop_obj = population[0], population[1]
            parent = [pop_dec[mating_pool, :], pop_obj[mating_pool, :]]
            offspring = Global.variation(parent[0],boundary=(Global.lower,Global.upper))
            population = [np.vstack((population[0], Global.individual(offspring)[0])), np.vstack((population[1], Global.individual(offspring)[1]))]
            population, front_no, crowd_dis,_ = environment_selection(population, Global.N)
            self.eva = self.eva - Global.N
            if self.eva%(10*evaluation/self.ite) == 0:
                end = time.clock()
                print('Running time %10.2f, percentage %s'%(end-start,100*(evaluation-self.eva)/evaluation))
        return population

    def draw(self):
        population = self.run()
        pop_obj = population[1]
        front_no, max_front = nd_sort(pop_obj, 1)
        non_dominated = pop_obj[front_no == 1, :]
        if Global.M == 2:
            plt.scatter(non_dominated[0, :], non_dominated[1, :])
        elif Global.M == 3:
            x, y, z = non_dominated[:, 0], non_dominated[:, 1], non_dominated[:, 2]
            ax = plt.subplot(111, projection='3d')
            ax.scatter(x, y, z, c='b')
        else:
            for i in range(len(non_dominated)):
                plt.plot(range(1, Global.M + 1), non_dominated[i, :])


a = nsgaii()
b=a.draw()
plt.show()