利用PSO算法实现无碰撞路径模型的python代码

以下是使用PSO算法实现无碰撞路径模型的Python代码示例：

import numpy as np
import math
import random

# 定义障碍物坐标
obstacle_x = [5, 10, 15, 20]
obstacle_y = [5, 10, 15, 20]

# 定义目标点和起点
start = (0, 0)
goal = (25, 25)

# 定义搜索空间的边界
x_min = 0
x_max = 30
y_min = 0
y_max = 30

# 定义PSO算法的参数
max_iter = 500  # 最大迭代次数
pop_size = 50  # 种群数量
w = 0.5  # 惯性权重
c1 = 1  # 自我认知系数
c2 = 1  # 社会认知系数

# 计算欧几里得距离
def euclidean_distance(a, b):
    return math.sqrt((a[0] - b[0]) ** 2 + (a[1] - b[1]) ** 2)

# 定义适应度函数
def fitness_function(position):
    total_distance = 0
    for i in range(len(position) - 1):
        distance = euclidean_distance(position[i], position[i + 1])
        for j in range(len(obstacle_x)):
            # 判断路径是否与障碍物相交
            if obstacle_x[j] <= position[i][0] <= obstacle_x[j] + 1 and obstacle_y[j] <= position[i][1] <= obstacle_y[j] + 1 \
            or obstacle_x[j] <= position[i + 1][0] <= obstacle_x[j] + 1 and obstacle_y[j] <= position[i + 1][1] <= obstacle_y[j] + 1:
                distance = float('inf')
                break
            if position[i][0] == position[i + 1][0]:
                if obstacle_x[j] <= position[i][0] <= obstacle_x[j] + 1 and min(position[i][1], position[i + 1][1]) <= obstacle_y[j] <= max(position[i][1], position[i + 1][1]):
                    distance = float('inf')
                    break
            else:
                slope = (position[i + 1][1] - position[i][1]) / (position[i + 1][0] - position[i][0])
                intercept = position[i][1] - slope * position[i][0]
                x = (obstacle_y[j] - intercept) / slope
                if obstacle_x[j] <= x <= obstacle_x[j] + 1 and min(position[i][0], position[i + 1][0]) <= x <= max(position[i][0], position[i + 1][0]):
                    distance = float('inf')
                    break
        total_distance += distance
    return 1 / total_distance

# 初始化种群位置和速度
def initialize_population(pop_size):
    population = []
    for i in range(pop_size):
        position = []
        for j in range(len(goal)):
            position.append((random.uniform(x_min, x_max), random.uniform(y_min, y_max)))
        population.append({'position': position, 'velocity': [0] * len(goal), 'fitness': 0})
    return population

# 更新适应度函数和最优解
def update_fitness(population, best_particle, best_fitness):
    for i in range(len(population)):
        fitness = fitness_function(population[i]['position'])
        population[i]['fitness'] = fitness
        if fitness > best_fitness:
            best_fitness = fitness
            best_particle = population[i]['position']
    return best_particle, best_fitness

# 更新粒子位置和速度
def update_particles(population, best_particle):
    for i in range(len(population)):
        for j in range(len(goal)):
            r1 = random.random()
            r2 = random.random()
            velocity = w * population[i]['velocity'][j] \
                       + c1 * r1 * (best_particle[j][0] - population[i]['position'][j][0]) \
                       + c2 * r2 * (population[i]['position'][j][0] - population[i]['position'][j][0])
            population[i]['velocity'][j] = velocity
            position = population[i]['position'][j][0] + velocity
            if position < x_min:
                position = x_min
            elif position > x_max:
                position = x_max
            population[i]['position'][j] = (position, population[i]['position'][j][1])
    return population

# PSO算法主函数
def PSO(max_iter, pop_size, w, c1, c2):
    population = initialize_population(pop_size)
    best_particle = population[0]['position']
    best_fitness = fitness_function(best_particle)
    for i in range(max_iter):
        best_particle, best_fitness = update_fitness(population, best_particle, best_fitness)
        population = update_particles(population, best_particle)
    return best_particle

# 运行PSO算法并输出结果
result = PSO(max_iter, pop_size, w, c1, c2)
print('最优路径：', result)
print('路径长度：', 1 / fitness_function(result))

代码中首先定义了障碍物的坐标、起点和目标点，以及搜索空间的边界和PSO算法的参数。然后定义了计算欧几里得距离的函数和适应度函数，其中适应度函数考虑了路径是否与障碍物相交。接着初始化种群位置和速度，并更新适应度函数和最优解。最后更新粒子位置和速度，并运行PSO算法并输出结果。