【python】智能优化.zip

import numpy as np import random ''' def fit_fun(X): A = 1 return 2 * A + X[0] ** 2 - A * np.cos(2 * np.pi * X[0]) + X[1] ** 2 - A * np.cos(2 * np.pi * X[1]) ''' def fit_fun(X): # 适应函数 return -np.abs(np.sin(X[0]) * np.cos(X[1]) * np.exp(np.abs(1 - np.sqrt(X[0] ** 2 + X[1] ** 2) / np.pi))) class Unit: # 初始化 def __init__(self, x_min, x_max, dim): self.__pos = np.array([x_min + random.random()*(x_max - x_min) for i in range(dim)]) self.__mutation = np.array([0.0 for i in range(dim)]) # 个体突变后的向量 self.__crossover = np.array([0.0 for i in range(dim)]) # 个体交叉后的向量 self.__fitnessValue = fit_fun(self.__pos) # 个体适应度 def set_pos(self, i, value): self.__pos[i] = value def get_pos(self): return self.__pos def set_mutation(self, i, value): self.__mutation[i] = value def get_mutation(self): return self.__mutation def set_crossover(self, i, value): self.__crossover[i] = value def get_crossover(self): return self.__crossover def set_fitness_value(self, value): self.__fitnessValue = value def get_fitness_value(self): return self.__fitnessValue class DE: def __init__(self, dim, size, iter_num, x_min, x_max, best_fitness_value=float('Inf'), F=0.5, CR=0.8): self.F = F self.CR = CR self.dim = dim # 维度 self.size = size # 总群个数 self.iter_num = iter_num # 迭代次数 self.x_min = x_min self.x_max = x_max self.best_fitness_value = best_fitness_value self.best_position = [0.0 for i in range(dim)] # 全局最优解 self.fitness_val_list = [] # 每次迭代最优适应值 # 对种群进行初始化 self.unit_list = [Unit(self.x_min, self.x_max, self.dim) for i in range(self.size)] def get_kth_unit(self, k): return self.unit_list[k] def set_bestFitnessValue(self, value): self.best_fitness_value = value def get_bestFitnessValue(self): return self.best_fitness_value def set_bestPosition(self, i, value): self.best_position[i] = value def get_bestPosition(self): return self.best_position # 变异 def mutation_fun(self): for i in range(self.size): r1 = r2 = r3 = 0 while r1 == i or r2 == i or r3 == i or r2 == r1 or r3 == r1 or r3 == r2: r1 = random.randint(0, self.size - 1) # 随机数范围为[0,size-1]的整数 r2 = random.randint(0, self.size - 1) r3 = random.randint(0, self.size - 1) mutation = self.get_kth_unit(r1).get_pos() + \ self.F * (self.get_kth_unit(r2).get_pos() - self.get_kth_unit(r3).get_pos()) for j in range(self.dim): # 判断变异后的值是否满足边界条件，不满足需重新生成 if self.x_min <= mutation[j] <= self.x_max: self.get_kth_unit(i).set_mutation(j, mutation[j]) else: rand_value = self.x_min + random.random()*(self.x_max - self.x_min) self.get_kth_unit(i).set_mutation(j, rand_value) # 交叉 def crossover(self): for unit in self.unit_list: for j in range(self.dim): rand_j = random.randint(0, self.dim - 1) rand_float = random.random() if rand_float <= self.CR or rand_j == j: unit.set_crossover(j, unit.get_mutation()[j]) else: unit.set_crossover(j, unit.get_pos()[j]) # 选择 def selection(self): for unit in self.unit_list: new_fitness_value = fit_fun(unit.get_crossover()) if new_fitness_value < unit.get_fitness_value(): unit.set_fitness_value(new_fitness_value) for i in range(self.dim): unit.set_pos(i, unit.get_crossover()[i]) if new_fitness_value < self.get_bestFitnessValue(): self.set_bestFitnessValue(new_fitness_value) for j in range(self.dim): self.set_bestPosition(j, unit.get_crossover()[j]) def update(self): for i in range(self.iter_num): self.mutation_fun() self.crossover() self.selection() self.fitness_val_list.append(self.get_bestFitnessValue()) return self.fitness_val_list, self.get_bestPosition()