智能算法之免疫遗传算法-TSP python 实现

import numpy as np class Population: def __init__(self, pop_size, chromosome_size): self.pop_size = pop_size self.chromosome_size = chromosome_size self.population = np.round(np.random.rand(pop_size, chromosome_size)).astype(np.int) self.fit_value = np.zeros((pop_size, 1)) def select_chromosome(self): total_fitness_value = self.fit_value.sum() p_fit_value = self.fit_value/total_fitness_value p_fit_value = np.cumsum(p_fit_value) point = np.sort(np.random.rand(self.pop_size, 1), 0) fit_in = 0 new_in = 0 new_population = np.zeros_like(self.population) while new_in < self.pop_size: if point[new_in] < p_fit_value[fit_in]: new_population[new_in, :] = self.population[fit_in, :] new_in += 1 else: fit_in += 1 self.population = new_population def cross_chromosome(self, cross_rate): x = self.pop_size y = self.chromosome_size new_population = np.zeros_like(self.population) for i in range(0, x-1, 2): if np.random.rand(1) < cross_rate: insert_point = int(np.round(np.random.rand(1) * y).item()) new_population[i, :] = np.concatenate([self.population[i, 0:insert_point], self.population[i+1, insert_point:y]], 0) new_population[i+1, :] = np.concatenate([self.population[i+1, 0:insert_point], self.population[i, insert_point:y]], 0) else: new_population[i, :] = self.population[i, :] new_population[i + 1, :] = self.population[i + 1, :] self.population = new_population def best(self): best_individual = self.population[0, :] best_fit = self.fit_value[0] for i in range(1, self.pop_size): if self.fit_value[i] > best_fit: best_individual = self.population[i, :] best_fit = self.fit_value[i] return best_individual, best_fit def mutation_chromosome(self, mutation_rate): x = self.pop_size for i in range(x): if np.random.rand(1) < mutation_rate: m_point = int(np.round(np.random.rand(1) * self.chromosome_size).item()) if self.population[i, m_point] == 1: self.population[i, m_point] = 0 else: self.population[i, m_point] = 1 def binary2decimal(self, population): pop1 = np.zeros_like(population) y = self.chromosome_size for i in range(y): pop1[:, i] = 2 ** (y - i - 1) * population[:, i] pop = np.sum(pop1, 1) pop2 = pop * 10/(1 << y) return pop2 def cal_obj_value(self): x = self.binary2decimal(self.population) self.fit_value = 10 * np.sin(5 * x) + 7 * np.abs(x - 5) + 10 if __name__ == "__main__": pop = Population(10, 3) a = np.random.rand(1, 1) b = np.random.rand(1, 2) c = np.concatenate([a, b], 1) print(c) point = np.random.rand(10, 1) point = np.sort(point, 0) print(point)