基于改进灰狼优化算法的路径规划python代码

以下是基于改进灰狼优化算法的路径规划的Python代码示例：

import numpy as np

# 定义问题
start = [0, 0]  # 起点坐标
end = [100, 100]  # 终点坐标
obstacles = [[30, 50], [60, 80], [20, 70]]  # 障碍物坐标列表

class Wolf:
    def __init__(self, position):
        self.position = position
        self.fitness = self.calculate_fitness()

    def calculate_fitness(self):
        # 计算适应度函数
        distance = np.sqrt((self.position[0] - end[0])**2 + (self.position[1] - end[1])**2)
        return distance

def initialize_population(population_size):
    # 初始化种群
    population = []
    for _ in range(population_size):
        x = np.random.uniform(start[0], end[0])
        y = np.random.uniform(start[1], end[1])
        wolf = Wolf([x, y])
        population.append(wolf)
    return population

def update_position(wolf, alpha, beta, delta):
    # 更新位置
    x1 = wolf.position
    x2 = alpha.position
    x3 = beta.position
    x4 = delta.position
    
    a1 = 2 * np.random.rand(2) - 1
    a2 = 2 * np.random.rand(2) - 1
    a3 = 2 * np.random.rand(2) - 1
    a4 = 2 * np.random.rand(2) - 1
    
    new_position = (x1 + a1 * (x2 - x3) + a2 * (x4 - x3)) / 2 + a3 * (x4 - x1)
    
    # 检查新位置是否在合法范围内
    new_position[0] = max(min(new_position[0], end[0]), start[0])
    new_position[1] = max(min(new_position[1], end[1]), start[1])
    
    # 检查新位置是否与障碍物发生碰撞
    for obstacle in obstacles:
        if np.sqrt((new_position[0] - obstacle[0])**2 + (new_position[1] - obstacle[1])**2) < 5:
            new_position = wolf.position
            break
    
    return new_position

def improve_wolves(population):
    # 改进灰狼位置
    sorted_population = sorted(population, key=lambda x: x.fitness)
    alpha = sorted_population[0]
    beta = sorted_population[1]
    delta = sorted_population[2]
    
    for i in range(len(population)):
        population[i].position = update_position(population[i], alpha, beta, delta)
        population[i].fitness = population[i].calculate_fitness()
    
    return population

def optimize_path(population_size, num_iterations):
    # 优化路径
    population = initialize_population(population_size)
    
    for _ in range(num_iterations):
        population = improve_wolves(population)
    
    sorted_population = sorted(population, key=lambda x: x.fitness)
    best_path = sorted_population[0].position
    
    return best_path

# 示例用法
best_path = optimize_path(population_size=50, num_iterations=100)

print("Best path:", best_path)

这是一个简单的基于改进灰狼优化算法的路径规划代码示例。代码中定义了问题（起点、终点和障碍物），并实现了灰狼的初始化、位置更新和改进等功能。最后，通过调用`optimize_path`函数来优化路径，并返回最佳路径。

请注意，这只是一个简单的示例代码，您可能需要根据您的具体问题进行适当的修改和调整。希望对您有所帮助！