111 lines
4.2 KiB
Python
111 lines
4.2 KiB
Python
import random
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import numpy as np
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class NSGA2:
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def __init__(self, pop_size, obj_num):
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self.pop_size = pop_size
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self.obj_num = obj_num
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def fast_non_dominated_sort(self, pop_obj):
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"""快速非支配排序"""
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pop_size = len(pop_obj)
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dominated = [[] for _ in range(pop_size)] # 被支配个体列表
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rank = [0] * pop_size # 个体的非支配等级
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n = [0] * pop_size # 支配该个体的个体数量
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# 计算每个个体的支配关系
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for p in range(pop_size):
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for q in range(pop_size):
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if p != q:
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# p支配q
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if all(pop_obj[p][i] <= pop_obj[q][i] for i in range(self.obj_num)) and \
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any(pop_obj[p][i] < pop_obj[q][i] for i in range(self.obj_num)):
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dominated[p].append(q)
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# q支配p
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elif all(pop_obj[q][i] <= pop_obj[p][i] for i in range(self.obj_num)) and \
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any(pop_obj[q][i] < pop_obj[p][i] for i in range(self.obj_num)):
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n[p] += 1
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# 找到等级为0的个体
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if n[p] == 0:
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rank[p] = 0
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# 计算其他等级
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current_rank = 0
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while True:
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next_rank = []
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for p in range(pop_size):
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if rank[p] == current_rank:
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for q in dominated[p]:
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n[q] -= 1
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if n[q] == 0 and rank[q] == 0:
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rank[q] = current_rank + 1
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next_rank.append(q)
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if not next_rank:
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break
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current_rank += 1
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return rank
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def crowding_distance(self, pop_obj, rank):
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"""计算拥挤度距离"""
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pop_size = len(pop_obj)
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distance = [0.0] * pop_size
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max_rank = max(rank) if rank else 0
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# 对每个等级的个体计算拥挤度
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for r in range(max_rank + 1):
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# 获取当前等级的个体索引
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current_indices = [i for i in range(pop_size) if rank[i] == r]
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if len(current_indices) <= 1:
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continue
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# 对每个目标函数进行排序
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for m in range(self.obj_num):
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# 按目标m的值排序
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sorted_indices = sorted(current_indices, key=lambda x: pop_obj[x][m])
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min_val = pop_obj[sorted_indices[0]][m]
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max_val = pop_obj[sorted_indices[-1]][m]
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# 边界个体的拥挤度设为无穷大
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distance[sorted_indices[0]] = float('inf')
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distance[sorted_indices[-1]] = float('inf')
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# 计算中间个体的拥挤度
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for i in range(1, len(sorted_indices) - 1):
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if max_val - min_val == 0:
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continue
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distance[sorted_indices[i]] += (pop_obj[sorted_indices[i + 1]][m] - pop_obj[sorted_indices[i - 1]][m]) / (max_val - min_val)
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return distance
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def selection(self, pop, pop_obj):
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"""选择操作：基于非支配排序和拥挤度的锦标赛选择，惩罚拥挤度为0的个体"""
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pop_size = len(pop)
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rank = self.fast_non_dominated_sort(pop_obj)
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distance = self.crowding_distance(pop_obj, rank)
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selected = []
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for _ in range(pop_size):
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# 随机选择两个个体进行锦标赛
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i = random.randint(0, pop_size - 1)
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j = random.randint(0, pop_size - 1)
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# 优先选择等级低（更优）的个体
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if rank[i] < rank[j]:
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selected.append(pop[i])
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elif rank[i] > rank[j]:
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selected.append(pop[j])
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# 等级相同则选择拥挤度大的个体（惩罚拥挤度为0的重复解）
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else:
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# 对拥挤度为0的个体进行惩罚
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if distance[i] == 0 and distance[j] > 0:
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selected.append(pop[j])
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elif distance[j] == 0 and distance[i] > 0:
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selected.append(pop[i])
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elif distance[i] >= distance[j]:
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selected.append(pop[i])
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else:
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selected.append(pop[j])
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return selected |