From 5b3723317854f0028cd4958733536f47af4e675b Mon Sep 17 00:00:00 2001 From: kellen Date: Sat, 15 Feb 2020 16:51:40 +0800 Subject: [PATCH] SA complete --- .idea/vcs.xml | 6 ++ .idea/workspace.xml | 80 +++++++++++-------- SA.py | 187 ++++++++++++++++++++++++++++++++++++++++++++ img/SA.png | Bin 0 -> 82592 bytes 4 files changed, 242 insertions(+), 31 deletions(-) create mode 100644 .idea/vcs.xml create mode 100755 SA.py create mode 100644 img/SA.png diff --git a/.idea/vcs.xml b/.idea/vcs.xml new file mode 100644 index 0000000..94a25f7 --- /dev/null +++ b/.idea/vcs.xml @@ -0,0 +1,6 @@ + + + + + + \ No newline at end of file diff --git a/.idea/workspace.xml b/.idea/workspace.xml index 09858ce..62019c0 100644 --- a/.idea/workspace.xml +++ b/.idea/workspace.xml @@ -1,13 +1,21 @@ - + + + + + + + + - + - + - + + - @@ -175,7 +183,7 @@ - + @@ -183,68 +191,72 @@ + + + + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + @@ -320,14 +332,19 @@ \ No newline at end of file diff --git a/SA.py b/SA.py new file mode 100755 index 0000000..5a9d1bb --- /dev/null +++ b/SA.py @@ -0,0 +1,187 @@ +import random +import math +import numpy as np +import matplotlib.pyplot as plt + + +class SA(object): + def __init__(self, num_city, data): + self.T0 = 2000 + self.Tend = 1e-3 + self.rate = 0.94 + self.size = 100 + self.num_city = num_city + self.scores = [] + self.location = data + self.ga_choose_ratio = 0.2 + self.mutate_ratio = 0.2 + # fruits中存每一个个体是下标的list + self.fire = self.random_init(num_city) + self.fires = [] + self.dis_mat = self.compute_dis_mat(num_city, data) + + # 显示初始化后的路径 + init_pathlen = 1. / self.compute_pathlen(self.fire, self.dis_mat) + init_best = self.location[self.fire] + init_best = np.vstack((init_best, init_best[0])) + plt.subplot(2, 2, 2) + plt.title('init best result') + plt.plot(init_best[:, 0], init_best[:, 1]) + # 存储存储每个温度下的最终路径,画出收敛图 + self.iter_x = [0] + self.iter_y = [1. / init_pathlen] + + # 初始化一条随机路径 + def random_init(self, num_city): + tmp = [x for x in range(num_city)] + random.shuffle(tmp) + return tmp + + # 计算不同城市之间的距离 + def compute_dis_mat(self, num_city, location): + dis_mat = np.zeros((num_city, num_city)) + for i in range(num_city): + for j in range(num_city): + if i == j: + dis_mat[i][j] = np.inf + continue + a = location[i] + b = location[j] + tmp = np.sqrt(sum([(x[0] - x[1]) ** 2 for x in zip(a, b)])) + dis_mat[i][j] = tmp + return dis_mat + + # 计算路径长度 + def compute_pathlen(self, path, dis_mat): + a = path[0] + b = path[-1] + result = dis_mat[a][b] + for i in range(len(path) - 1): + a = path[i] + b = path[i + 1] + result += dis_mat[a][b] + return result + + # 计算一个温度下产生的一个群体的长度 + def compute_paths(self, paths): + result = [] + for one in paths: + length = self.compute_pathlen(one, self.dis_mat) + result.append(length) + return result + + # 产生一个新的解:随机交换两个元素的位置 + def get_new_fire(self, fire): + + t = [x for x in range(len(fire))] + a, b = np.random.choice(t, 2) + # 1.交换两个元素的位置,效果不好 + # fire[a], fire[b] = fire[b], fire[a] + # return fire + + # 2片段逆转,效果比1好 + x = min(a, b) + y = max(a, b) + a = fire[:x] + b = fire[x:y][::-1] + c = fire[y:] + return a + b + c + + # 退火策略,根据温度变化有一定概率接受差的解 + def eval_fire(self, raw, get, temp): + len1 = self.compute_pathlen(raw, self.dis_mat) + len2 = self.compute_pathlen(get, self.dis_mat) + dc = len2 - len1 + p = max(1e-4, np.exp(-dc / temp)) + if len2 < len1: + return get + elif np.random.rand() <= p: + return get + else: + return raw + + # 模拟退火总流程 + def sa(self): + count = 0 + # 记录最优解 + best_path = self.fire + best_length = self.compute_pathlen(self.fire, self.dis_mat) + + while self.T0 > self.Tend: + count += 1 + # 产生在这个温度下的一些随机解 + tmp_fires = [] + for _ in range(self.size): + tmp_new = self.get_new_fire(self.fire.copy()) + self.fire = self.eval_fire(self.fire, tmp_new, self.T0) + tmp_fires.append(tmp_new) + # 记录当前温度下的最优解 + lengths = self.compute_paths(tmp_fires) + min_length = min(lengths) + min_index = lengths.index(min_length) + min_path = tmp_fires[min_index] + # 更新最优解 + if min_length < best_length: + best_length = min_length + best_path = min_path + # 降低温度 + self.T0 *= self.rate + # 记录路径收敛曲线 + self.iter_x.append(count) + self.iter_y.append(best_length) + return best_length, best_path + + def run(self): + best_length, best_path = self.sa() + plt.subplot(2, 2, 4) + plt.title('convergence curve') + plt.plot(self.iter_x, self.iter_y) + return self.location[best_path], best_length + + +# 读取数据 +def read_tsp(path): + lines = open(path, 'r').readlines() + assert 'NODE_COORD_SECTION\n' in lines + index = lines.index('NODE_COORD_SECTION\n') + data = lines[index + 1:-1] + tmp = [] + for line in data: + line = line.strip().split(' ') + if line[0] == 'EOF': + continue + tmpline = [] + for x in line: + if x == '': + continue + else: + tmpline.append(float(x)) + if tmpline == []: + continue + tmp.append(tmpline) + data = tmp + return data + + +data = read_tsp('data/st70.tsp') + +data = np.array(data) +plt.suptitle('SA in st70.tsp') +data = data[:, 1:] +plt.subplot(2, 2, 1) +plt.title('raw data') +show_data = np.vstack([data, data[0]]) +plt.plot(data[:, 0], data[:, 1]) +Best, Best_path = math.inf, None + +foa = SA(num_city=data.shape[0], data=data.copy()) +path, path_len = foa.run() +if path_len < Best: + Best = path_len + Best_path = path +plt.subplot(2, 2, 3) +# 加上一行因为会回到起点 +Best_path = np.vstack([Best_path, Best_path[0]]) +plt.plot(Best_path[:, 0], Best_path[:, 1]) +plt.title('result') +plt.show() diff --git a/img/SA.png b/img/SA.png new file mode 100644 index 0000000000000000000000000000000000000000..ec74243c6e8a40e73b6893c606f160eaaca7e3a1 GIT binary patch literal 82592 zcma&NWl$V#v@MLg6Wl$40Kwe}5+DS3m*DR1?i$=7I0Schhv4q+3_kF6-gCaH`|sXA z)G$@k)z9uNYpuOH{Kt1mR3t(q2nYyNX(@3<2nZ-Y2nfh61SsH});gIw2nat2X>k!{ zH{G)=L^qZB`3Lux1&bFI@J8da3g4Qlr8UE3!?SAnyk@4RR^>wN_uNXQtg?La;g4bD zht=|9n@5rke=InnyJ3(thxorRUxfPNLvd!+l&+8DW zemc$nK1bU4$#nm_z;^Ffu5KFr|1P$jPqc+q^S>8HwL@I`-^;@NcUjc@f`a$w#jdv_ 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