基于Python的粒子群算法实现进化算法求解TSP问题的完整代码。代码中从编号1-30，生成了30个坐标固定的城市，并求解从点1出发，经过其他29个点后的最优路径（包含最优路径图、最优路径编号序列、最优路径长度三部分内容）。

以下是基于Python的粒子群算法实现进化算法求解TSP问题的完整：

import numpy as np
import matplotlib.pyplot as plt

# 定义城市数量
num_cities = 30

# 定义城市坐标
cities = np.array([[ 37,  52], [ 49,  49], [ 52,  64], [ 20,  26], [ 40,  30],
                   [ 21,  47], [ 17,  63], [ 31,  62], [ 52,  33], [ 51,  21],
                   [ 42,  41], [ 31,  32], [  5,  25], [ 12,  42], [ 36,  16],
                   [ 52,  41], [ 27,  23], [ 17,  33], [ 13,  13], [ 57,  58],
                   [ 62,  42], [ 42,  57], [ 16,  57], [  8,  52], [  7,  38],
                   [ 27,  68], [ 30,  48], [ 43,  67], [ 58,  48], [ 58,  27]])

# 定义距离矩阵
dist_matrix = np.zeros((num_cities, num_cities))
for i in range(num_cities):
    for j in range(num_cities):
        dist_matrix[i][j] = np.sqrt((cities[i][0]-cities[j][0])**2 + (cities[i][1]-cities[j][1])**2)

# 定义粒子群算法相关参数
num_particles = 100
max_iter = 100
w = 0.8
c1 = 1.5
c2 = 1.5

# 初始化粒子群位置和速度
particles_pos = np.zeros((num_particles, num_cities), dtype=int)
particles_vel = np.zeros((num_particles, num_cities), dtype=int)
for i in range(num_particles):
    particles_pos[i] = np.random.permutation(num_cities)
    particles_vel[i] = np.random.permutation(num_cities)

# 定义全局最优解及其路径长度
global_best_pos = np.zeros(num_cities, dtype=int)
global_best_cost = float('inf')

# 迭代寻优
for iter in range(max_iter):
    # 计算每个粒子的路径长度
    particles_cost = np.zeros(num_particles)
    for i in range(num_particles):
        for j in range(num_cities-1):
            particles_cost[i] += dist_matrix[particles_pos[i][j]][particles_pos[i][j+1]]
        particles_cost[i] += dist_matrix[particles_pos[i][num_cities-1]][particles_pos[i][0]]
        # 更新全局最优解
        if particles_cost[i] < global_best_cost:
            global_best_pos = particles_pos[i].copy()
            global_best_cost = particles_cost[i]
    
    # 更新每个粒子的速度和位置
    for i in range(num_particles):
        r1, r2 = np.random.rand(), np.random.rand()
        particles_vel[i] = w*particles_vel[i] + c1*r1*(particles_pos[i]-particles_pos[i]) + c2*r2*(global_best_pos-particles_pos[i])
        particles_pos[i] = np.argsort(particles_vel[i])
    
    # 输出每次迭代的全局最优解
    print('Iteration:', iter+1, 'Global Best Cost:', global_best_cost)

# 绘制最优路径图
x = cities[:,0]
y = cities[:,1]
plt.plot(x[global_best_pos], y[global_best_pos], 'r', alpha=0.5)
plt.scatter(x, y)
plt.show()

# 输出最优路径编号序列和路径长度
print('Optimal Path:', global_best_pos+1)
print('Optimal Path Length:', global_best_cost)

运行以上代码，可以得到最优路径图、最优路径编号序列和最优路径长度三部分结果。