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import math
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import random
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import matplotlib.pyplot as plt
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import copy
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# 用的是ZDT2测试函数
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# First function to optimize
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def function1(x):
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return x[0]
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# Second function to optimize
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def function2(x):
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gx = funcationG(x)
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value = 1 - (x[0] / gx) ** 2
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return gx * value
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def funcationG(x):
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sumX = 0
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for i in range(1, varNum):
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sumX = sumX + x[i]
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return 1 + (9 * sumX) / (varNum - 1)
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# Function to find index of list
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def index_of(a, list):
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for i in range(0, len(list)):
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if list[i] == a:
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return i
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return -1
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# Function to sort by values
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def sort_by_values(list1, values):
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sorted_list = []
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while (len(sorted_list) != len(list1)):
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if index_of(min(values), values) in list1:
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sorted_list.append(index_of(min(values), values))
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values[index_of(min(values), values)] = math.inf
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return sorted_list
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# Function to carry out NSGA-II's fast non dominated sort
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def fast_non_dominated_sort(values1, values2):
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S = [[] for i in range(0, len(values1))] # 解所支配的集合
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front = [[]] # 排序结果
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n = [0 for i in range(0, len(values1))] # 支配者数量
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rank = [0 for i in range(0, len(values1))]
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for p in range(0, len(values1)):
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S[p] = []
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n[p] = 0
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for q in range(0, len(values1)):
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if (values1[p] < values1[q] and values2[p] < values2[q]) or (
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values1[p] <= values1[q] and values2[p] < values2[q]) or (
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values1[p] < values1[q] and values2[p] <= values2[q]):
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if q not in S[p]:
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S[p].append(q)
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elif (values1[q] < values1[p] and values2[q] < values2[p]) or (
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values1[q] <= values1[p] and values2[q] < values2[p]) or (
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values1[q] < values1[p] and values2[q] <= values2[p]):
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n[p] = n[p] + 1
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if n[p] == 0:
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rank[p] = 0
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if p not in front[0]:
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front[0].append(p)
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i = 0
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while (front[i] != []):
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Q = []
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for p in front[i]:
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for q in S[p]:
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n[q] = n[q] - 1
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if (n[q] == 0):
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rank[q] = i + 1
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if q not in Q:
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Q.append(q)
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i = i + 1
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front.append(Q)
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del front[len(front) - 1]
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return front
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# Function to calculate crowding distance 拥挤度距离计算
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def crowding_distance(values1, values2, front):
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distance = [0 for i in range(0, len(front))]
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sorted1 = sort_by_values(front, values1[:])
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sorted2 = sort_by_values(front, values2[:])
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distance[0] = math.inf
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distance[len(front) - 1] = math.inf
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for k in range(1, len(front) - 1):
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distance[k] = distance[k] + (values1[sorted1[k + 1]] - values1[sorted1[k - 1]]) / (max(values1) - min(values1))
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for k in range(1, len(front) - 1):
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distance[k] = distance[k] + (values2[sorted2[k + 1]] - values2[sorted2[k - 1]]) / (max(values2) - min(values2))
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return distance
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# 二进制交叉
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def crossover(individuala, individualb, a, b):
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individual1 = copy.deepcopy(individuala)
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individual2 = copy.deepcopy(individualb)
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for j in range(min(a, b), max(a, b) + 1):
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u = random.random()
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if u < 0.5:
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r = (2 * u) ** (1 / (NC + 1))
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else:
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r = (1 / (2 * (1 - u))) ** (1 / (NC + 1))
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individual1[j] = 0.5 * ((1 + r) * individual1[j] + (1 - r) * individual2[j])
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individual2[j] = 0.5 * ((1 - r) * individual1[j] + (1 + r) * individual2[j])
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individual1[j] = 1 if individual1[j] > 1 else individual1[j] # 此处需要修改为常量
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individual2[j] = 1 if individual2[j] > 1 else individual2[j]
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individual1[j] = 0 if individual1[j] < 0 else individual1[j]
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individual2[j] = 0 if individual2[j] < 0 else individual2[j]
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return individual1, individual2
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# 多项式变异
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def mutation(individual, a):
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individualTemp = copy.deepcopy(individual)
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u = random.random()
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if u < 0.5:
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r = (2 * u) ** (1 / (NM + 1)) - 1
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else:
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r = (1 - (2 * (1 - u))) ** (1 / (NM + 1))
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individualTemp[a] = individualTemp[a] + r
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individualTemp[a] = 1 if individualTemp[a] > 1 else individualTemp[a] # 此处需要修改为常量
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individualTemp[a] = 0 if individualTemp[a] < 0 else individualTemp[a]
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return individualTemp
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# 使用此函数之前要确保非支配排序解中都按照拥挤度排序过了
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def competition(non_dominated_sorted, numOfSelect):
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selectionResult = []
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non_dominated_sortedTemp = []
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for i in range(0, len(non_dominated_sorted)):
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for j in range(0, len(non_dominated_sorted[i])):
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non_dominated_sortedTemp.append(non_dominated_sorted[i][j])
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while len(selectionResult) < numOfSelect:
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selections = [random.randint(0, popSize - 1) for i in range(0, popSize // 2)]
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selectionResult.append(non_dominated_sortedTemp[min(selections)])
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return selectionResult
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min_x = 0
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max_x = 1 # x1 属于[0,1]
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varNum = 30 # xi 中i的最大个数
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popSize = 100 # 种群个数
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max_gen = 500 # 最大迭代次数
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pc = 0.9 # 交叉
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pm = 0.01 # 变异
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NC = 20
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NM = 20
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# 初始化种群
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solution = []
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for i in range(0, popSize):
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solution.append([random.random() for j in range(0, varNum)])
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times = 0
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non_dominated_sorted_solution = []
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function1_values = []
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function2_values = []
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while times < max_gen:
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# 计算种群中，每个个体的两个目标函数值
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function1_values = [function1(solution[i]) for i in range(0, popSize)]
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function2_values = [function2(solution[i]) for i in range(0, popSize)]
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# 快速非支配排序
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non_dominated_sorted_solution = fast_non_dominated_sort(function1_values[:], function2_values[:])
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# 计算拥挤度
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crowding_distance_values = []
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for i in range(0, len(non_dominated_sorted_solution)):
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crowding_distance_values.append(
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crowding_distance(function1_values[:], function2_values[:], non_dominated_sorted_solution[i][:]))
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# 按照拥挤度排序
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for i in range(0, len(non_dominated_sorted_solution)):
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non_dominated_sorted_solution2_1 = [
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index_of(non_dominated_sorted_solution[i][j], non_dominated_sorted_solution[i]) for j in
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range(0, len(non_dominated_sorted_solution[i]))]
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front22 = sort_by_values(non_dominated_sorted_solution2_1[:], crowding_distance_values[i][:]) # 按照拥挤度排序
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non_dominated_sorted_solution[i] = [non_dominated_sorted_solution[i][front22[j]] for j in
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range(0, len(non_dominated_sorted_solution[i]))]
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non_dominated_sorted_solution[i].reverse() # 翻转之后是从大到小排序，拥挤度
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solution2 = copy.deepcopy(solution)
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# 竞标赛选取
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offSpring = competition(non_dominated_sorted_solution, popSize)
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# 交叉
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for i in range(0, len(offSpring) // 2):
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rc = random.random()
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if (rc < pc):
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index1 = random.randint(0, varNum - 1)
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index2 = random.randint(0, varNum - 1)
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while index1 == index2:
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index1 = random.randint(0, varNum - 1)
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indi1, indi2 = crossover(solution[offSpring[i]], solution[offSpring[len(offSpring) - i - 1]], index1,
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index2)
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solution2.append(indi1)
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solution2.append(indi2)
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# 变异
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for i in range(0, len(offSpring)):
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rm = random.random()
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if rm < pm:
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indexOfM = random.randint(0, varNum - 1)
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muIndi = mutation(solution[offSpring[i]], indexOfM)
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solution2.append(muIndi)
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# 计算函数值
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lengthOfSolution2 = len(solution2)
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function1_values2 = [function1(solution2[i]) for i in range(0, lengthOfSolution2)]
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function2_values2 = [function2(solution2[i]) for i in range(0, lengthOfSolution2)]
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# 非支配快速排序
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non_dominated_sorted_solution2 = fast_non_dominated_sort(function1_values2[:], function2_values2[:])
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# 计算拥挤度
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crowding_distance_values2 = []
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for i in range(0, len(non_dominated_sorted_solution2)):
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crowding_distance_values2.append(
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crowding_distance(function1_values2[:], function2_values2[:], non_dominated_sorted_solution2[i][:]))
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# 产生新的种群
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new_solution = []
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for i in range(0, len(non_dominated_sorted_solution2)):
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non_dominated_sorted_solution2_1 = [
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index_of(non_dominated_sorted_solution2[i][j], non_dominated_sorted_solution2[i]) for j in
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range(0, len(non_dominated_sorted_solution2[i]))]
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front22 = sort_by_values(non_dominated_sorted_solution2_1[:], crowding_distance_values2[i][:]) # 按照拥挤度排序
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front = [non_dominated_sorted_solution2[i][front22[j]] for j in
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range(0, len(non_dominated_sorted_solution2[i]))]
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front.reverse() # 翻转之后是从大到小排序，拥挤度
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for value in front:
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new_solution.append(value)
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if (len(new_solution) == popSize):
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break
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if (len(new_solution) == popSize):
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break
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solution = [solution2[i] for i in new_solution]
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times = times + 1
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print('This is ', times, '\n')
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# Lets plot the final front now
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minF1 = []
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minF2 = []
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for k in non_dominated_sorted_solution[0]:
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minF1.append(function1_values[k])
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minF2.append(function2_values[k])
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# 打印第一层
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print("The best front for Generation number ", times, " is")
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for valuez in non_dominated_sorted_solution[0]:
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print(solution[valuez:], end=" ")
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print("\n")
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plt.xlabel('F 1', fontsize=15)
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plt.ylabel('F 2', fontsize=15)
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plt.scatter(minF1, minF2)
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plt.show()
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