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76 lines
2.3 KiB
76 lines
2.3 KiB
11 months ago
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import random
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def initialize_population(num_points, pop_size):
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population = []
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for _ in range(pop_size):
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individual = list(range(num_points))
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random.shuffle(individual)
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population.append(individual)
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return population
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def evaluate_fitness(individual, distances):
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fitness = 0
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num_points = len(individual)
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for i in range(num_points):
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city1 = individual[i]
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city2 = individual[(i + 1) % num_points]
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fitness += distances[city1][city2]
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return fitness
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def crossover(parent1, parent2):
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child = [-1] * len(parent1)
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start_idx = random.randint(0, len(parent1)-1)
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end_idx = random.randint(start_idx+1, len(parent1))
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child[start_idx:end_idx] = parent1[start_idx:end_idx]
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for city in parent2:
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if city not in child:
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for i in range(len(child)):
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if child[i] == -1:
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child[i] = city
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break
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return child
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def mutate(individual):
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idx1, idx2 = random.sample(range(len(individual)), 2)
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individual[idx1], individual[idx2] = individual[idx2], individual[idx1]
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def genetic_algorithm(distances, pop_size, num_generations):
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num_points = len(distances)
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population = initialize_population(num_points, pop_size)
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for generation in range(num_generations):
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population_fitness = []
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for individual in population:
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fitness = evaluate_fitness(individual, distances)
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population_fitness.append((individual, fitness))
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population_fitness.sort(key=lambda x: x[1]) # 按适应度排序
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elite_individual = population_fitness[0][0]
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new_population = [elite_individual]
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while len(new_population) < pop_size:
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parent1, parent2 = random.choices(population_fitness, k=2)
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child = crossover(parent1[0], parent2[0])
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mutate(child)
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new_population.append(child)
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population = new_population
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best_individual = population_fitness[0][0]
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return best_individual
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# 示例使用
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# distances为距离矩阵
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distances = [
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[0, 50, 99, 0, 64, 0],
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[50, 0, 12, 45, 74, 13],
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[99, 12, 0, 25, 0, 61],
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[0, 45, 25, 0, 45, 47],
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[64, 74, 0, 45, 0, 0],
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[0, 13, 61, 47, 0, 0]
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]
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population_size = 100
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num_generations = 1000
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best_solution = genetic_algorithm(distances, population_size, num_generations)
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print(best_solution)
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