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c375cb3c02
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34ebe2b065
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import random
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import math
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import numpy as np
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import matplotlib.pyplot as plt
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import time
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class TS(object):
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def __init__(self, num_city, data,taboo_size=5, iteration=500):
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self.taboo_size = taboo_size
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self.iteration = iteration
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self.num_city = num_city
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self.location = data
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self.taboo = []
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self.dis_mat = self.compute_dis_mat(num_city, data)
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self.path = self.greedy_init(self.dis_mat,100,num_city)
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self.best_path = self.path
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self.cur_path = self.path
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self.best_length = self.compute_pathlen(self.path, self.dis_mat)
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# 显示初始化后的路径
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init_pathlen = 1. / self.compute_pathlen(self.path, self.dis_mat)
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# 存储结果，画出收敛图
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self.iter_x = [0]
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self.iter_y = [1. / init_pathlen]
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def greedy_init(self, dis_mat, num_total, num_city):
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start_index = 0
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result = []
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for i in range(num_total):
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rest = [x for x in range(0, num_city)]
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if start_index >= num_city:
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start_index = np.random.randint(0, num_city)
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result.append(result[start_index].copy())
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continue
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current = start_index
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rest.remove(current)
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result_one = [current] # 找到一条最近邻路径
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while len(rest) != 0:
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tmp_min = math.inf
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tmp_choose = -1
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for x in rest:
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if dis_mat[current][x] < tmp_min:
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tmp_min = dis_mat[current][x]
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tmp_choose = x
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current = tmp_choose
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result_one.append(tmp_choose)
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rest.remove(tmp_choose)
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result.append(result_one)
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start_index += 1
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pathlens = self.compute_paths(result)
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sortindex = np.argsort(pathlens)
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index = sortindex[0]
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return result[index]
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# return result[0]
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# 初始化一条随机路径
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def random_init(self, num_city):
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tmp = [x for x in range(num_city)]
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random.shuffle(tmp)
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return tmp
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# 计算不同城市之间的距离
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def compute_dis_mat(self, num_city, location):
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dis_mat = np.zeros((num_city, num_city))
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for i in range(num_city):
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for j in range(num_city):
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if i == j:
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dis_mat[i][j] = np.inf
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continue
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a = location[i]
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b = location[j]
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tmp = np.sqrt(sum([(x[0] - x[1]) ** 2 for x in zip(a, b)]))
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dis_mat[i][j] = tmp
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return dis_mat
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# 计算路径长度
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def compute_pathlen(self, path, dis_mat):
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a = path[0]
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b = path[-1]
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result = dis_mat[a][b]
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for i in range(len(path) - 1):
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a = path[i]
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b = path[i + 1]
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result += dis_mat[a][b]
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return result
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# 计算一个群体的长度
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def compute_paths(self, paths):
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result = []
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for one in paths:
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length = self.compute_pathlen(one, self.dis_mat)
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result.append(length)
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return result
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# 产生随机解
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def ts_search(self, x):
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moves = []
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new_paths = []
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while len(new_paths)<400:
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i = np.random.randint(len(x))
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j = np.random.randint(len(x))
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tmp = x.copy()
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tmp[i:j] = tmp[i:j][::-1]
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new_paths.append(tmp)
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moves.append([i, j])
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return new_paths, moves
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# 禁忌搜索
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def ts(self):
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start_time = time.time()
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for cnt in range(self.iteration):
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new_paths, moves = self.ts_search(self.cur_path)
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new_lengths = self.compute_paths(new_paths)
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sort_index = np.argsort(new_lengths)
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min_l = new_lengths[sort_index[0]]
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min_path = new_paths[sort_index[0]]
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min_move = moves[sort_index[0]]
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# 更新当前的最优路径
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if min_l < self.best_length:
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self.best_length = min_l
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self.best_path = min_path
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self.cur_path = min_path
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# 更新禁忌表
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if min_move in self.taboo:
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self.taboo.remove(min_move)
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self.taboo.append(min_move)
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else:
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# 找到不在禁忌表中的操作
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while min_move in self.taboo:
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sort_index = sort_index[1:]
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min_path = new_paths[sort_index[0]]
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min_move = moves[sort_index[0]]
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self.cur_path = min_path
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self.taboo.append(min_move)
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# 禁忌表超长了
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if len(self.taboo) > self.taboo_size:
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self.taboo = self.taboo[1:]
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self.iter_x.append(cnt)
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self.iter_y.append(self.best_length)
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#print(cnt, self.best_length)
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print(self.best_length)
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total_time = time.time() - start_time
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return self.best_length, total_time
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def run(self):
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best_length, total_time = self.ts()
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return self.location[self.best_path], best_length, total_time
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def read_tsp(path):
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lines = open(path, 'r').readlines()
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assert 'NODE_COORD_SECTION\n' in lines
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index = lines.index('NODE_COORD_SECTION\n')
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data = lines[index + 1:-1]
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tmp = []
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for line in data:
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line = line.strip().split(' ')
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if line[0] == 'EOF':
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continue
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tmpline = []
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for x in line:
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if x == '':
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continue
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else:
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tmpline.append(float(x))
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if tmpline == []:
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continue
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tmp.append(tmpline)
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data = tmp
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return data
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def parameter_sensitivity_test():
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# 定义参数组合
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taboo_sizes = [3, 5, 10]
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iterations = [200, 500, 1000]
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results = []
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# 遍历所有参数组合
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for taboo_size in taboo_sizes:
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for iteration in iterations:
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model = TS(num_city=data.shape[0], data=data.copy(),
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taboo_size=taboo_size, iteration=iteration)
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best_length, total_time = model.ts() # 调用ts()并获取结果
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results.append({
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"taboo_size": taboo_size,
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"iteration": iteration,
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"best_length": best_length,
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"time": total_time
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})
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print(f"taboo_size={taboo_size}, iteration={iteration}, "
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f"length={best_length:.2f}, time={total_time:.2f}s")
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# 结果可视化（示例：热力图）
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import pandas as pd
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import seaborn as sns
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df = pd.DataFrame(results)
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pivot_length = df.pivot(index="taboo_size", columns="iteration", values="best_length")
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pivot_time = df.pivot(index="taboo_size", columns="iteration", values="time")
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plt.figure(figsize=(12, 5))
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plt.subplot(1, 2, 1)
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sns.heatmap(pivot_length, annot=True, fmt=".2f", cmap="YlGnBu")
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plt.title("Path Length vs Parameters")
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plt.subplot(1, 2, 2)
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sns.heatmap(pivot_time, annot=True, fmt=".2f", cmap="YlGnBu")
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plt.title("Time Cost vs Parameters")
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plt.tight_layout()
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plt.show()
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if __name__ == "__main__":
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data = read_tsp('data/st70.tsp')
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data = np.array(data)
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plt.suptitle('TS in st70.tsp')
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data = data[:, 1:]
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plt.subplot(2, 2, 1)
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plt.title('raw data')
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show_data = np.vstack([data, data[0]])
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plt.plot(data[:, 0], data[:, 1])
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model = TS(num_city=data.shape[0], data=data.copy())
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Best_path, Best_length,execution_time = model.run()
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print("时间：",execution_time)
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Best_path = np.vstack([Best_path, Best_path[0]])
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fig, axs = plt.subplots(2, 1, sharex=False, sharey=False)
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axs[0].scatter(Best_path[:, 0], Best_path[:,1])
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Best_path = np.vstack([Best_path, Best_path[0]])
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axs[0].plot(Best_path[:, 0], Best_path[:, 1])
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axs[0].set_title('规划结果')
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iterations = model.iter_x
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best_record = model.iter_y
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axs[1].plot(iterations, best_record)
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axs[1].set_title('收敛曲线')
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#plt.show()
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plt.show(block=False) # 添加此行，允许后续代码继续执行
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#参数敏感性实验
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parameter_sensitivity_test()
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