遗传算法求解路径规划的代码
时间: 2023-09-13 15:03:03 浏览: 80
基于遗传算法的路径规划算法代码matlab代码
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遗传算法是一种优化算法,可以用于求解路径规划问题。下面是一个使用遗传算法求解路径规划的示例代码(假设路径规划问题是在一个二维空间中寻找最短路径):
```python
import random
# 生成随机个体
def generate_individual(length):
return [random.randint(0, 100) for _ in range(length)]
# 生成初始种群
def generate_population(pop_size, ind_length):
return [generate_individual(ind_length) for _ in range(pop_size)]
# 计算个体适应度(路径长度)
def calculate_fitness(individual):
# 此处根据具体问题定义适应度函数
# 这里假设路径长度越短适应度越高
fitness = 0
for i in range(len(individual)-1):
x1, y1 = individual[i]
x2, y2 = individual[i+1]
dist = ((x2 - x1)**2 + (y2 - y1)**2)**0.5
fitness += dist
return fitness
# 选择操作(轮盘赌算法)
def selection(population):
fitness_values = [calculate_fitness(individual) for individual in population]
total_fitness = sum(fitness_values)
probabilities = [fitness / total_fitness for fitness in fitness_values]
selected_indices = random.choices(range(len(population)), probabilities, k=2)
return [population[i] for i in selected_indices]
# 交叉操作(单点交叉)
def crossover(parent1, parent2):
point = random.randint(1, len(parent1)-1)
child1 = parent1[:point] + parent2[point:]
child2 = parent2[:point] + parent1[point:]
return child1, child2
# 变异操作(随机交换两个位置的值)
def mutation(individual, mutation_rate):
for i in range(len(individual)):
if random.random() < mutation_rate:
swap_index = random.randint(0, len(individual)-1)
individual[i], individual[swap_index] = individual[swap_index], individual[i]
return individual
# 遗传算法主程序
def genetic_algorithm(pop_size, ind_length, num_generations, crossover_rate, mutation_rate):
population = generate_population(pop_size, ind_length)
for _ in range(num_generations):
next_generation = []
while len(next_generation) < pop_size:
parent1, parent2 = selection(population)
if random.random() < crossover_rate:
offspring1, offspring2 = crossover(parent1, parent2)
next_generation.append(mutation(offspring1, mutation_rate))
next_generation.append(mutation(offspring2, mutation_rate))
else:
next_generation.append(mutation(parent1, mutation_rate))
next_generation.append(mutation(parent2, mutation_rate))
population = next_generation
best_individual = min(population, key=calculate_fitness)
return best_individual
# 使用示例
pop_size = 100
ind_length = 10
num_generations = 100
crossover_rate = 0.8
mutation_rate = 0.1
best_path = genetic_algorithm(pop_size, ind_length, num_generations, crossover_rate, mutation_rate)
print("Best path:", best_path)
```
以上是一个简单的使用遗传算法求解路径规划问题的示例代码,代码中仅包含了基本的算法流程,实际应用中需根据具体问题进行适当的调整和优化。
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