From 2933b4c887954e164a9844a9476d87eabe1c2183 Mon Sep 17 00:00:00 2001 From: kellen Date: Sun, 16 Feb 2020 22:21:24 +0800 Subject: [PATCH] complete init1 --- .idea/workspace.xml | 197 ++++++++------- ACO.py | 187 ++++++++++++++ GA.py | 2 + PSO.py | 235 ++++++++++++++++++ README.md | 38 +++ SA.py | 2 - SOM.py | 206 ++++++++++++++++ TS.py | 169 +++++++++++++ data/bayg29.tsp.gz | Bin 1196 -> 0 bytes data/berlin52.tsp | 60 ----- data/berlin52.tsp.gz | Bin 472 -> 0 bytes data/burma14.tsp | 24 -- data/burma14.tsp.gz | Bin 307 -> 0 bytes data/ch130.tsp | 137 ----------- data/ch130.tsp.gz | Bin 2475 -> 0 bytes data/ch150.tsp | 157 ------------ data/ch150.tsp.gz | Bin 2847 -> 0 bytes data/gr96.tsp | 104 -------- data/gr96.tsp.gz | Bin 899 -> 0 bytes data/kroA100.tsp | 107 -------- data/kroA100.tsp.gz | Bin 792 -> 0 bytes data/kroA200.tsp | 207 ---------------- data/kroA200.tsp.gz | Bin 1436 -> 0 bytes data/kroB200.tsp | 207 ---------------- data/kroB200.tsp.gz | Bin 1434 -> 0 bytes data/pcb442.tsp | 449 ---------------------------------- data/pcb442.tsp.gz | Bin 2337 -> 0 bytes data/pr226.tsp | 233 ------------------ data/pr226.tsp.gz | Bin 1162 -> 0 bytes data/pr76.tsp | 83 ------- data/pr76.tsp.gz | Bin 533 -> 0 bytes data/rat99.tsp | 106 -------- data/rat99.tsp.gz | Bin 639 -> 0 bytes data/st70.tsp.gz | Bin 471 -> 0 bytes data/tsp225.tsp | 232 ------------------ data/tsp225.tsp.gz | Bin 1244 -> 0 bytes foa_2opt_greedy.py | 261 -------------------- foa_2opt_greedy_ga.py | 386 ----------------------------- foa_2opt_greedy_morebest.py | 232 ------------------ foa_2opt_greedy_multi_best.py | 234 ------------------ foa_3opt_greedy.py | 269 -------------------- foa_3opt_greedy_bak.py | 225 ----------------- foa_greedy_single_GA.py | 297 ---------------------- foa_shuffle_part.py | 235 ------------------ foa_shufllepart_greedy.py | 187 -------------- img/ACO.png | Bin 0 -> 71574 bytes img/PSO.png | Bin 0 -> 83346 bytes img/SI.png | Bin 0 -> 83147 bytes img/SOM.png | Bin 0 -> 81802 bytes img/TS.png | Bin 0 -> 83918 bytes 50 files changed, 951 insertions(+), 4517 deletions(-) create mode 100755 ACO.py create mode 100755 PSO.py create mode 100644 README.md create mode 100755 SOM.py create mode 100755 TS.py delete mode 100755 data/bayg29.tsp.gz delete mode 100755 data/berlin52.tsp delete mode 100755 data/berlin52.tsp.gz delete mode 100755 data/burma14.tsp delete mode 100755 data/burma14.tsp.gz delete mode 100755 data/ch130.tsp delete mode 100755 data/ch130.tsp.gz delete mode 100755 data/ch150.tsp delete mode 100755 data/ch150.tsp.gz delete mode 100755 data/gr96.tsp delete mode 100755 data/gr96.tsp.gz delete mode 100755 data/kroA100.tsp delete mode 100755 data/kroA100.tsp.gz delete mode 100755 data/kroA200.tsp delete mode 100755 data/kroA200.tsp.gz delete mode 100755 data/kroB200.tsp delete mode 100755 data/kroB200.tsp.gz delete mode 100755 data/pcb442.tsp delete mode 100755 data/pcb442.tsp.gz delete mode 100755 data/pr226.tsp delete mode 100755 data/pr226.tsp.gz delete mode 100755 data/pr76.tsp delete mode 100755 data/pr76.tsp.gz delete mode 100755 data/rat99.tsp delete mode 100755 data/rat99.tsp.gz delete mode 100755 data/st70.tsp.gz delete mode 100755 data/tsp225.tsp delete mode 100755 data/tsp225.tsp.gz delete mode 100755 foa_2opt_greedy.py delete mode 100755 foa_2opt_greedy_ga.py delete mode 100755 foa_2opt_greedy_morebest.py delete mode 100755 foa_2opt_greedy_multi_best.py delete mode 100755 foa_3opt_greedy.py delete mode 100755 foa_3opt_greedy_bak.py delete mode 100755 foa_greedy_single_GA.py delete mode 100755 foa_shuffle_part.py delete mode 100755 foa_shufllepart_greedy.py create mode 100644 img/ACO.png create mode 100644 img/PSO.png create mode 100644 img/SI.png create mode 100644 img/SOM.png create mode 100644 img/TS.png diff --git a/.idea/workspace.xml b/.idea/workspace.xml index 62019c0..0672e7e 100644 --- a/.idea/workspace.xml +++ b/.idea/workspace.xml @@ -2,9 +2,27 @@ - - + + + + + + + + + + + + + + + + + + + + - - + + - + - + - + - + - + + + + - - - @@ -183,13 +201,27 @@ - + + + + + @@ -199,64 +231,72 @@ - + + - + - + + - + - + + - + - + + - + - + + - + - + + - + - + + - + - + + - + @@ -264,63 +304,23 @@ - + - + - + - + - - file://$PROJECT_DIR$/foa_2opt_greedy_ga.py - 290 - - - file://$PROJECT_DIR$/foa_shuffle_part.py - 154 - - - file://$PROJECT_DIR$/foa_2opt_greedy.py - 152 - - - file://$PROJECT_DIR$/foa_2opt_greedy_morebest.py - 130 - - - file://$PROJECT_DIR$/foa_3opt_greedy.py - 160 - - - file://$PROJECT_DIR$/foa_3opt_greedy.py - 138 - - - file://$PROJECT_DIR$/foa_3opt_greedy.py - 167 - - - file://$PROJECT_DIR$/foa_2opt_greedy.py - 118 - file://$PROJECT_DIR$/GA.py 122 @@ -346,6 +346,31 @@ 97 + + file://$PROJECT_DIR$/ACO.py + 102 + + + file://$PROJECT_DIR$/PSO.py + 177 + + + file://$PROJECT_DIR$/PSO.py + 88 + + + file://$PROJECT_DIR$/PSO.py + 93 + + + file://$PROJECT_DIR$/TS.py + 106 + @@ -359,20 +384,26 @@ + + + + + + + - - - + - - + + + \ No newline at end of file diff --git a/ACO.py b/ACO.py new file mode 100755 index 0000000..b6cd2a0 --- /dev/null +++ b/ACO.py @@ -0,0 +1,187 @@ +import random +import math +import numpy as np +import matplotlib.pyplot as plt + + +class ACO(object): + def __init__(self, num_city, data): + self.m = 30 # 蚂蚁数量 + self.alpha = 1 # 信息素重要程度因子 + self.beta = 5 # 启发函数重要因子 + self.rho = 0.1 # 信息素挥发因子 + self.Q = 1 # 常量系数 + self.num_city = num_city # 城市规模 + self.location = data # 城市坐标 + self.Tau = np.ones([num_city, num_city]) # 信息素矩阵 + self.Table = [[0 for _ in range(num_city)] for _ in range(self.m)] # 生成的蚁群 + self.iter = 1 + self.iter_max = 500 + self.dis_mat = self.compute_dis_mat(num_city, self.location) # 计算城市之间的距离矩阵 + self.Eta = 10. / self.dis_mat # 启发式函数 + self.paths = None # 蚁群中每个个体的长度 + # 存储存储每个温度下的最终路径，画出收敛图 + self.iter_x = [] + self.iter_y = [] + + # 轮盘赌选择 + def rand_choose(self, p): + x = np.random.rand() + for i, t in enumerate(p): + x -= t + if x <= 0: + break + return i + + # 生成蚁群 + def get_ants(self, num_city): + for i in range(self.m): + start = np.random.randint(num_city - 1) + self.Table[i][0] = start + unvisit = list([x for x in range(num_city) if x != start]) + current = start + j = 1 + while len(unvisit) != 0: + P = [] + # 通过信息素计算城市之间的转移概率 + for v in unvisit: + P.append(self.Tau[current][v] ** self.alpha * self.Eta[current][v] ** self.beta) + P_sum = sum(P) + P = [x / P_sum for x in P] + # 轮盘赌选择一个一个城市 + index = self.rand_choose(P) + current = unvisit[index] + self.Table[i][j] = current + unvisit.remove(current) + j += 1 + + # 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 + + # 计算一条路径的长度 + 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 + + # 更新信息素 + def update_Tau(self): + delta_tau = np.zeros([self.num_city, self.num_city]) + paths = self.compute_paths(self.Table) + for i in range(self.m): + for j in range(self.num_city - 1): + a = self.Table[i][j] + b = self.Table[i][j + 1] + delta_tau[a][b] = delta_tau[a][b] + self.Q / paths[i] + a = self.Table[i][0] + b = self.Table[i][-1] + delta_tau[a][b] = delta_tau[a][b] + self.Q / paths[i] + self.Tau = (1 - self.rho) * self.Tau + delta_tau + + def aco(self): + best_lenth = math.inf + best_path = None + for cnt in range(self.iter_max): + # 生成新的蚁群 + self.get_ants(self.num_city) # out>>self.Table + self.paths = self.compute_paths(self.Table) + # 取该蚁群的最优解 + tmp_lenth = min(self.paths) + tmp_path = self.Table[self.paths.index(tmp_lenth)] + # 可视化初始的路径 + if cnt == 0: + init_show = self.location[tmp_path] + init_show = np.vstack([init_show, init_show[0]]) + plt.subplot(2, 2, 2) + plt.title('init best result') + plt.plot(init_show[:, 0], init_show[:, 1]) + # 更新最优解 + if tmp_lenth < best_lenth: + best_lenth = tmp_lenth + best_path = tmp_path + # 更新信息素 + self.update_Tau() + + # 保存结果 + self.iter_x.append(cnt) + self.iter_y.append(best_lenth) + print(cnt) + return best_lenth, best_path + + def run(self): + best_length, best_path = self.aco() + 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('ACO in st70.tsp') +data = data[:, 1:] +plt.subplot(2, 2, 1) +plt.title('raw data') +# 加上一行因为会回到起点 +show_data = np.vstack([data, data[0]]) +plt.plot(data[:, 0], data[:, 1]) + +aco = ACO(num_city=data.shape[0], data=data.copy()) +Best_path, Best = aco.run() +print(Best) +plt.subplot(2, 2, 3) + + +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/GA.py b/GA.py index 3783c43..b2b4b42 100755 --- a/GA.py +++ b/GA.py @@ -205,6 +205,8 @@ def run(self): if tmp_best_score > best_score: best_score = tmp_best_score BEST_LIST = tmp_best_one + print(i) + print(1./best_score) plt.subplot(2, 2, 4) plt.title('convergence curve') plt.plot(self.iter_x, self.iter_y) diff --git a/PSO.py b/PSO.py new file mode 100755 index 0000000..6ae4a75 --- /dev/null +++ b/PSO.py @@ -0,0 +1,235 @@ +import random +import math +import numpy as np +import matplotlib.pyplot as plt + + +class PSO(object): + def __init__(self, num_city, data): + self.iter_max = 500 # 迭代数目 + self.num = 150 # 粒子数目 + self.num_city = num_city # 城市数 + self.location = data # 城市的位置坐标 + # 计算距离矩阵 + self.dis_mat = self.compute_dis_mat(num_city, self.location) # 计算城市之间的距离矩阵 + # 初始化所有粒子 + self.particals = self.random_init(self.num, num_city) + self.lenths = self.compute_paths(self.particals) + # 得到初始化群体的最优解 + init_l = min(self.lenths) + init_index = self.lenths.index(init_l) + init_path = self.particals[init_index] + # 画出初始的路径图 + init_show = self.location[init_path] + plt.subplot(2, 2, 2) + plt.title('init best result') + plt.plot(init_show[:, 0], init_show[:, 1]) + # 记录每个个体的当前最优解 + self.local_best = self.particals + self.local_best_len = self.lenths + # 记录当前的全局最优解,长度是iteration + self.global_best = init_path + self.global_best_len = init_l + # 输出解 + self.best_l = self.global_best_len + self.best_path = self.global_best + # 存储每次迭代的结果，画出收敛图 + self.iter_x = [0] + self.iter_y = [init_l] + + # 随机初始化 + def random_init(self, num_total, num_city): + tmp = [x for x in range(num_city)] + result = [] + for i in range(num_total): + random.shuffle(tmp) + result.append(tmp.copy()) + return result + + # 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 + + # 计算一条路径的长度 + 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 + + # 评估当前的群体 + def eval_particals(self): + min_lenth = min(self.lenths) + min_index = self.lenths.index(min_lenth) + cur_path = self.particals[min_index] + # 更新当前的全局最优 + if min_lenth < self.global_best_len: + self.global_best_len = min_lenth + self.global_best = cur_path + # 更新当前的个体最优 + for i, l in enumerate(self.lenths): + if l < self.local_best_len[i]: + self.local_best_len[i] = l + self.local_best[i] = self.particals[i] + + # 粒子交叉 + def cross(self, cur, best): + one = cur.copy() + l = [t for t in range(self.num_city)] + t = np.random.choice(l,2) + x = min(t) + y = max(t) + cross_part = best[x:y] + tmp = [] + for t in one: + if t in cross_part: + continue + tmp.append(t) + # 两种交叉方法 + one = tmp + cross_part + l1 = self.compute_pathlen(one, self.dis_mat) + one2 = cross_part + tmp + l2 = self.compute_pathlen(one2, self.dis_mat) + if l1 self.taboo_size: + self.taboo = self.taboo[1:] + self.iter_x.append(cnt) + self.iter_y.append(self.best_length) + print(cnt, self.best_length) + print(self.best_length) + + def run(self): + self.ts() + plt.subplot(2, 2, 4) + plt.title('convergence curve') + plt.plot(self.iter_x, self.iter_y) + return self.location[self.best_path], self.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('TS 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]) + +func = TS(num_city=data.shape[0], data=data.copy()) +best_path, best_length = func.run() +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/data/bayg29.tsp.gz b/data/bayg29.tsp.gz deleted file mode 100755 index 0a9f4c36e1fda1bcffa429f78f6a8af625818b34..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 1196 zcmV;d1XKGTiwFq0$G|NB17cx$XEHf1baQY3RaRY3Bu5N1s%Mes11o$79Pxm-3}9$&0J$)DT*9;{EBZ)<#PZr0Pu z)y2i?a`Q>7%gO$Azk8AWll;2d`tJJbRuJcA{pI-E-Rbe>cpTl_UY*_z{KN5^)yW1oPSTs$ zTtrhBS*l2##cakKm@1xv?q&v28e6mWG#zfye=c(O0M9Tn5Ttybq;NDw{&oY1_CbBi)++Q$+aVfQj0l$gNL@nXAVJ~=s1ea$&`toQIBwZi^H z{+abbjHDBB)`uFCr@|xT###t!Y@Icmo#)#<4f=IOZuA{F&0Z_1*stWu9QnQYy++^U zsA-~C3a~u8Hw5wN2dnW-jfMVa?Pm52;&CrErrz2-4|R{D2YT}QAt>I(`-Xgco&I58 zc;C>sWlA;=&2#X}%(+Gk+^yusde;+_T1`?l;z1XP=(9krxrc@Q 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random -import numpy as np -import math -import pandas as pd -import tqdm - - -class FOA(object): - def __init__(self, num_city, num_total, iteration, data, save_path, save_freq, **kwargs): - self.num_city = num_city - self.num_total = num_total - self.iteration = iteration - self.save_path = save_path - self.save_freq = save_freq - self.location = data - self.dis_mat = self.compute_dis_mat(num_city, self.location) - # self.fruits = self.greedy_init(self.dis_mat, num_total, num_city) - self.fruits = self.random_init(num_total,num_city) - - # 贪心初始化种群 - def greedy_init(self, dis_mat, num_total, num_city): - start_index = 0 - result = [] - for i in range(num_total): - rest = [x for x in range(0, num_city)] - # 所有起始点都已经生成了 - if start_index >= num_city: - start_index = np.random.randint(0, num_city) - result.append(result[start_index].copy()) - continue - current = start_index - rest.remove(current) - # 找到一条最近邻路径 - result_one = [current] - while len(rest) != 0: - tmp_min = math.inf - tmp_choose = -1 - for x in rest: - if dis_mat[current][x] < tmp_min: - tmp_min = dis_mat[current][x] - tmp_choose = x - - current = tmp_choose - result_one.append(tmp_choose) - rest.remove(tmp_choose) - result.append(result_one) - start_index += 1 - return result - - # def greedy_init_2(self,dis_mat,num_total,num_city): - # start_index = 0 - # result = [[]]*num_total - # rest = [x for x in range(0, num_city)] - # # 所有起始点都已经生成了 - # if start_index >= num_city: - # start_index = np.random.randint(0,num_city) - # result[i] = result[start_index].copy() - # continue - # current = start_index - # rest.remove(current) - # # 找到一条最近邻路径 - # result_one = [current] - # while len(rest) != 0: - # tmp_min = math.inf - # tmp_choose = -1 - # for x in rest: - # if dis_mat[current][x] < tmp_min: - # tmp_min = dis_mat[current][x] - # tmp_choose = x - # - # current = tmp_choose - # result_one.append(tmp_choose) - # rest.remove(tmp_choose) - # # result[i] = result_one - # result[0] = result_one - # start_index += 1 - # return result - # 随机初始化种群 - def random_init(self, num_total, num_city): - tmp = [x for x in range(num_city)] - result = [] - for i in range(num_total): - random.shuffle(tmp) - result.append(tmp.copy()) - return result - - # 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): - try: - a = path[0] - b = path[-1] - except: - import pdb - pdb.set_trace() - 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 - - # 计算种群适应度 - def compute_adp(self, fruits): - adp = [] - for fruit in fruits: - if isinstance(fruit, int): - import pdb - pdb.set_trace() - length = self.compute_pathlen(fruit, self.dis_mat) - adp.append(1.0 / length) - return np.array(adp) - - # 对序列做2opt操作 - def _2opt(self, x): - city_list = [i for i in range(self.num_city)] - choice = np.random.choice(city_list, 2) - choice.sort() - i, j = choice - a = x[:i] - b = x[i:j] - c = x[j:] - b = b[::-1] - if isinstance(x, int) or isinstance(a + b + c, int): - import pdb - pdb.set_trace() - adp1 = 1. / self.compute_pathlen(x, self.dis_mat) - adp2 = 1. / self.compute_pathlen(a + b + c, self.dis_mat) - if adp1 > adp2: - result = x - else: - result = a + b + c - - # result = a+b+c - return result - - # 迭代一次，返回适应度最高的个体和他的序列。 - def fly(self, fruits, num_total): - # 选择适应度最高的个体 - self.dis_adp = self.compute_adp(self.fruits) - # 从高到底适应度排序 - sortindex = np.argsort(-self.dis_adp) - best_adp = self.dis_adp[sortindex[0]] - best_fruit = self.fruits[sortindex[0]] - # 保留种群中适应度最好的个体 - # self.fruits[0] = best_fruit - self.fruits = [best_fruit] - for i in range(1, num_total): - # 2opt生成一个新个体 - - opt_2_res = self._2opt(best_fruit.copy()) - self.fruits.append(opt_2_res) - return best_adp, best_fruit - - # 总共的迭代过程，返回最终最好的长度，以及一段路径 - def run(self): - best_fruit = None - best_adp = -math.inf - for i in range(self.iteration): - tmp_adp, tmp_list = self.fly(self.fruits, self.num_total) - if tmp_adp > best_adp: - best_adp = tmp_adp - best_fruit = tmp_list - bestlen = 1.0 / best_adp - return self.location[best_fruit], bestlen - - -import pandas as pd - - -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) -data = data[:, 1:] -result_ = [] -runtimes = 20 -import time - -start_time = time.time() -for _ in tqdm.trange(runtimes): - foa = FOA(num_city=data.shape[0], num_total=50, iteration=1000, data=data.copy(), save_path='sdsdsdssd.txt', - save_freq=10) - res, length = foa.run() - result_.append(length) -end_time = time.time() -result_ = np.array(result_) - -print('best:{:.2f}\tworst:{:.2f}\tmean:{:.2f}\tstd:{:.2f}\taverage_time:{:.2f}'.format(result_.min(), result_.max(), - result_.mean(), result_.std(), ( - end_time - start_time) / runtimes)) - -# import os -# # print(os.getcwd()) -# import matplotlib.pyplot as plt -# import numpy as np -# from mpl_toolkits.mplot3d import Axes3D -# -# fig = plt.figure() -# ax = Axes3D(fig) -# colors = ('#377eb8', '#ff7f00', '#4daf4a','#aaaaaa') -# def plot(i, data): -# -# x = data[:,0] -# y = data[:,1] -# z = data[:,2] -# ax.scatter(x, y, z,color=colors[i],marker = '+') -# # ax.set_zlabel('Z', fontdict={'size': 15, 'color': 'red'}) -# # ax.set_ylabel('Y', fontdict={'size': 15, 'color': 'red'}) -# # ax.set_xlabel('X', fontdict={'size': 15, 'color': 'red'}) -# ax.plot3D(data[:,0], data[:,1], data[:,2],color = colors[i]) -# result = [] -# for i in range(4): -# data = pd.read_csv('path5_{}.csv'.format(i),header=None) -# data = np.array(data) -# foa = FOA(num_city=data.shape[0],num_total=20,iteration=100,adp_norm=1000,data=data,save_path='sdsdsdssd.txt',save_freq=10) -# res,lenth = foa.run() -# -# res = np.array(res).astype(np.int) -# plot(i,res) -# -# print(lenth) -# # result.append(res) -# # result = np.concatenate(result,axis = 0) -# # print(result.shape) -# # plot(result) -# plt.show() diff --git a/foa_2opt_greedy_ga.py b/foa_2opt_greedy_ga.py deleted file mode 100755 index ddf0dc1..0000000 --- a/foa_2opt_greedy_ga.py +++ /dev/null @@ -1,386 +0,0 @@ -import random -import numpy as np -import math -import pandas as pd - - -class FOA(object): - def __init__(self, num_city, num_total, iteration, adp_norm, data, save_path, save_freq, **kwargs): - self.num_city = num_city - self.num_total = num_total - self.dis_adp = [0 for _ in range(self.num_total)] - self.iteration = iteration - self.adp_norm = adp_norm - self.save_path = save_path - self.save_freq = save_freq - self.location = data - self.dis_mat = self.distance_p2p_mat() - # self.generate_best() - self.ga_choose_num = 10 - self.ga_ratio = 0.5 - self.cross_ratio = 0.9 - self.mutate_ratio = 0.9 - self.generate_fruits() - print('get data,she shape is {}'.format(self.location.shape)) - - def get_data(self, data): - return np.loadtxt(data)[:, 1:] - - # 贪心初始化一条最优路径 - def generate_best(self,flag = True): - if flag: - initial = [0] - rest = [x for x in range(1, self.num_city)] - initial_path = 0 - while len(rest) != 0: - start = initial[-1] - tmp_min = math.inf - tmp_choose = -1 - for x in rest: - if self.dis_mat[start][x] < tmp_min: - tmp_min = self.dis_mat[start][x] - tmp_choose = x - initial_path += tmp_min - initial.append(tmp_choose) - rest.remove(tmp_choose) - initial_path += self.dis_mat[tmp_choose][0] - return initial, initial_path - else: - initial = [x for x in range(self.num_city)] - initial_path = 0 - for index in range(self.num_city - 1): - initial_path += self.dis_mat[index][index+1] - initial_path += self.dis_mat[index][0] - return initial, initial_path - - def generate_fruits(self): - best, initial_path = self.generate_best(True) - self.fruits = [np.array(best)] - for _ in range(self.num_total - 1): - tmp = best.copy() - np.random.shuffle(tmp) - self.fruits.append(np.array(tmp)) - - # 对称矩阵，两个城市之间的距离 支持2d和3d - def distance_p2p_mat(self): - dis_mat = [] - for i in range(self.num_city): - dis_mat_each = [] - for j in range(self.num_city): - tmp = 0 - for k in range(len(self.location[i])): - tmp += pow(self.location[i][k] - self.location[j][k], 2) - tmp = math.sqrt(tmp) - dis_mat_each.append(tmp) - dis_mat.append(dis_mat_each) - return dis_mat - - # 目标函数计算,适应度计算，中间计算。适应度为1/总距离*10000 - def dis_adp_total(self, ): - self.dis_adp = [] - for i in range(self.num_total): - try: - dis = self.dis_mat[self.fruits[i][self.num_city - 1]][self.fruits[i][0]] # 回家 - except: - import pdb - pdb.set_trace() - for j in range(self.num_city - 1): - dis = self.dis_mat[self.fruits[i][j]][self.fruits[i][j + 1]] + dis - dis_adp_each = self.adp_norm / dis - self.dis_adp.append(dis_adp_each) - - def swap_part(self, list1, list2): - index = len(list1) - list = list1 + list2 - list = list[::-1] - return list[:index], list[index:] - - def ga_cross(self, x, y): - x = np.array(x) - y = np.array(y) - len_ = len(x) - assert len(x) == len(y) - path_list = [t for t in range(len_)] - # start, end = np.random.choice(path_list,2) - # order = list(random.sample(path_list,2)) - start = np.random.randint(len_-5) - end = start +5 - # order.sort() - # start, end = order - - # 找到冲突点并存下他们的下标,x中存储的是y中的下标 - tmp = x[start:end] - x_conflict_index = [] - for sub in tmp: - index = int(np.where(y == sub)[0]) - if not (index >= start and index < end): - x_conflict_index.append(index) - y_confict_index = [] - tmp = y[start:end] - for sub in tmp: - index = int(np.where(x == sub)[0]) - if not (index >= start and index < end): - y_confict_index.append(index) - assert len(x_conflict_index) == len(y_confict_index) - # 交叉 - tmp = x[start:end].copy() - x[start:end] = y[start:end] - y[start:end] = tmp - # 解决冲突 - for index in range(len(x_conflict_index)): - i = x_conflict_index[index] - j = y_confict_index[index] - y[i], x[j] = x[j], y[i] - - assert len(set(x)) == len_ and len(set(y)) == len_ - return x, y - - def ga_cross(self, x, y): - x = np.array(x) - y = np.array(y) - len_ = len(x) - assert len(x) == len(y) - path_list = [t for t in range(len_)] - # start, end = np.random.choice(path_list,2) - order = list(random.sample(path_list,2)) - # start = np.random.randint(len_-3) - # end = start +3 - order.sort() - start, end = order - - # 找到冲突点并存下他们的下标,x中存储的是y中的下标 - tmp = x[start:end] - x_conflict_index = [] - for sub in tmp: - index = int(np.where(y == sub)[0]) - if not (index >= start and index < end): - x_conflict_index.append(index) - y_confict_index = [] - tmp = y[start:end] - for sub in tmp: - index = int(np.where(x == sub)[0]) - if not (index >= start and index < end): - y_confict_index.append(index) - assert len(x_conflict_index) == len(y_confict_index) - # 交叉 - tmp = x[start:end].copy() - x[start:end] = y[start:end] - y[start:end] = tmp - # 解决冲突 - for index in range(len(x_conflict_index)): - i = x_conflict_index[index] - j = y_confict_index[index] - y[i], x[j] = x[j], y[i] - - assert len(set(x)) == len_ and len(set(y)) == len_ - return list(x), list(y) - - def ga_parent(self,scores,choose_num): - scores_order = np.argsort(scores)[::-1].copy() - scores_order = scores_order[0:choose_num] - genes_choose = [] - genes_score = [] - for sub in scores_order: - genes_choose.append(self.fruits[sub]) - genes_score.append(scores[sub]) - return genes_score, genes_choose - - def ga_choose(self,genes_score,genes_choose): - sum_score = sum(genes_score) - score_ratio = [sub*1.0/sum_score for sub in genes_score] - rand1 = np.random.rand() - rand2 = np.random.rand() - for i, sub in enumerate(score_ratio): - if rand1 >= 0: - rand1 -= sub - if rand1 < 0: - index1 = i - if rand2 >= 0: - rand2 -= sub - if rand2 < 0: - index2 = i - if rand1 < 0 and rand2 < 0: - break - return list(genes_choose[index1]), list(genes_choose[index2]) - - def ga_mutate(self,gene): - path_list = [t for t in range(len(gene))] - order = list(random.sample(path_list, 2)) - start, end = min(order), max(order) - tmp = gene[start:end] - # np.random.shuffle(tmp) - tmp = tmp[::-1] - gene[start:end] = tmp - return list(gene) - - def ga(self): - # 获得优质父代 - scores = self.dis_adp - genes_score, genes_choose = self.ga_parent(scores,self.ga_choose_num) - ga_result = [] - # for sub in genes_choose[0:self.ga_choose_num]: - # ga_result.append(list(sub)) - # 轮盘赌方式对父代进行选择 - gene_x, gene_y = self.ga_choose(genes_score,genes_choose) - # 交叉 - if np.random.rand() < self.cross_ratio: - gene_x, gene_y = self.ga_cross(gene_x, gene_y) - # 变异 - if np.random.rand() < self.mutate_ratio: - gene_x_new = self.ga_mutate(gene_x) - if np.random.rand() < self.mutate_ratio: - gene_y_new = self.ga_mutate(gene_y) - - if not (gene_x in ga_result): - ga_result.append(gene_x) - if not (gene_y in ga_result): - ga_result.append(gene_y) - - ga_result = ga_result[:self.num_total] - ga_result = [np.array(x) for x in ga_result] - return ga_result - - def get_best(self): - self.dis_adp_total() - sortindex = np.argsort(self.dis_adp)[::-1] - best_adp = self.dis_adp[sortindex[0]] - best_fruit = self.fruits[sortindex[0]] - return best_adp, best_fruit - - - def opt_2(self, x): - city_list = [i for i in range(self.num_city)] - choice = np.random.choice(city_list, 2) - choice.sort() - res_list = [] - a, b = list(choice) - part1 = list(x[:a]) - part2 = list(x[a:b]) - part3 = list(x[b:]) - - # 1 - res = part1 + part2[::-1] + part3 - res_list.append(res) - # 2 - list1, list2 = self.swap_part(part1, part3) - res = list1 + part2 + list2 - res_list.append(res) - # 3 - res = list1 + part2[::-1] + list2 - res_list.append(res) - return res_list - - def fly(self): - self.dis_adp_total() - sortindex = np.argsort(self.dis_adp)[::-1] - best_adp = self.dis_adp[sortindex[0]] - best_fruit = self.fruits[sortindex[0]] - fruits = [best_fruit] - while len(fruits) < self.num_total*2: - if np.random.rand() < self.ga_ratio: - ga_result = self.ga() - fruits += ga_result - else: - opt_2_result = self.opt_2(best_fruit) - fruits += opt_2_result - - self.dis_adp_total() - sortindex = np.argsort(self.dis_adp)[::-1] - keep_index = sortindex[:self.num_total] - - self.fruits = np.array(fruits)[keep_index] - self.fruits = list(fruits) - - return best_adp, best_fruit - - def run(self): - print('==================Running the code==================') - BEST_LIST = None - best_adp = -math.inf - - with open(self.save_path, 'w') as f: - for i in range(1, self.iteration + 1): - max_adp, best_list = self.fly() - # self.ga() - if max_adp > best_adp: - best_adp = max_adp - BEST_LIST = best_list - bestlen = self.adp_norm / best_adp - return self.location[BEST_LIST], bestlen - - -import pandas as pd - -# x = [t for t in range(10)] -# yy = x.copy() -# np.random.shuffle(yy) -# -# tx,ty = FOA.ga_cross(x,yy) -# print(tx,ty) - - -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(' ') - tmpline = [] - for x in line: - if x == '': - continue - else: - tmpline.append(int(x)) - tmp.append(tmpline) - data = tmp - return data - - -data = read_tsp('data/st70.tsp') -data = np.array(data) -data = data[:, 1:] -for _ in range(10): - foa = FOA(num_city=data.shape[0], num_total=200, iteration=1000, adp_norm=1000, data=data.copy(), - save_path='sdsdsdssd.txt', save_freq=10) - res, result = foa.run() - print(result) - - - -# import os -# # print(os.getcwd()) -# import matplotlib.pyplot as plt -# import numpy as np -# from mpl_toolkits.mplot3d import Axes3D -# -# fig = plt.figure() -# ax = Axes3D(fig) -# colors = ('#377eb8', '#ff7f00', '#4daf4a','#aaaaaa') -# def plot(i, data): -# -# x = data[:,0] -# y = data[:,1] -# z = data[:,2] -# ax.scatter(x, y, z,color=colors[i],marker = '+') -# # ax.set_zlabel('Z', fontdict={'size': 15, 'color': 'red'}) -# # ax.set_ylabel('Y', fontdict={'size': 15, 'color': 'red'}) -# # ax.set_xlabel('X', fontdict={'size': 15, 'color': 'red'}) -# ax.plot3D(data[:,0], data[:,1], data[:,2],color = colors[i]) -# result = [] -# for i in range(4): -# data = pd.read_csv('path5_{}.csv'.format(i),header=None) -# data = np.array(data) -# foa = FOA(num_city=data.shape[0],num_total=20,iteration=100,adp_norm=1000,data=data,save_path='sdsdsdssd.txt',save_freq=10) -# res,lenth = foa.run() -# -# res = np.array(res).astype(np.int) -# plot(i,res) -# -# print(lenth) -# # result.append(res) -# # result = np.concatenate(result,axis = 0) -# # print(result.shape) -# # plot(result) -# plt.show() diff --git a/foa_2opt_greedy_morebest.py b/foa_2opt_greedy_morebest.py deleted file mode 100755 index 97bae6f..0000000 --- a/foa_2opt_greedy_morebest.py +++ /dev/null @@ -1,232 +0,0 @@ -import random -import numpy as np -import math -import pandas as pd -import tqdm - -class FOA(object): - def __init__(self, num_city, num_total, iteration, data, save_path, save_freq, **kwargs): - self.num_city = num_city - self.num_total = num_total - self.iteration = iteration - self.save_path = save_path - self.save_freq = save_freq - self.location = data - self.dis_mat = self.compute_dis_mat(num_city,self.location) - self.fruits = self.greedy_init(self.dis_mat,num_total,num_city) - # self.fruits = self.random_init(num_total,num_city) - # 贪心初始化种群 - def greedy_init(self,dis_mat,num_total,num_city): - start_index = 0 - result = [[]]*num_total - for i in range(num_total): - rest = [x for x in range(0, num_city)] - # 所有起始点都已经生成了 - if start_index >= num_city: - start_index = np.random.randint(0,num_city) - result[i] = result[start_index].copy() - continue - current = start_index - rest.remove(current) - # 找到一条最近邻路径 - result_one = [current] - while len(rest) != 0: - tmp_min = math.inf - tmp_choose = -1 - for x in rest: - if dis_mat[current][x] < tmp_min: - tmp_min = dis_mat[current][x] - tmp_choose = x - - current = tmp_choose - result_one.append(tmp_choose) - rest.remove(tmp_choose) - result[i] = result_one - start_index += 1 - return result - # def greedy_init_2(self,dis_mat,num_total,num_city): - # start_index = 0 - # result = [[]]*num_total - # rest = [x for x in range(0, num_city)] - # # 所有起始点都已经生成了 - # if start_index >= num_city: - # start_index = np.random.randint(0,num_city) - # result[i] = result[start_index].copy() - # continue - # current = start_index - # rest.remove(current) - # # 找到一条最近邻路径 - # result_one = [current] - # while len(rest) != 0: - # tmp_min = math.inf - # tmp_choose = -1 - # for x in rest: - # if dis_mat[current][x] < tmp_min: - # tmp_min = dis_mat[current][x] - # tmp_choose = x - # - # current = tmp_choose - # result_one.append(tmp_choose) - # rest.remove(tmp_choose) - # # result[i] = result_one - # result[0] = result_one - # start_index += 1 - # return result - # 随机初始化种群 - def random_init(self,num_total,num_city): - tmp = [x for x in range(num_city)] - result = [[]]*num_total - for i in range(num_total): - random.shuffle(tmp) - result[i] = tmp - return result - # 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]) ** 2for 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 - # 计算种群适应度 - def compute_adp(self,fruits): - adp = [] - for fruit in fruits: - length = self.compute_pathlen(fruit,self.dis_mat) - adp.append(1.0/length) - return np.array(adp) - # 对序列做2opt操作 - def _2opt(self, x): - city_list = [i for i in range(self.num_city)] - choice = np.random.choice(city_list, 2) - choice.sort() - i,j = choice - a = x[:i] - b = x[i:j] - c = x[j:] - b = b[::-1] - result = a+b+c - return result - # 迭代一次，返回适应度最高的个体和他的序列。 - def fly(self,fruits,num_total): - best_ratio = 0.6 - best_num =best_ratio*num_total - start_ge = best_num - # 选择适应度最高的个体 - self.dis_adp = self.compute_adp(self.fruits) - # 从高到底适应度排序 - sortindex = np.argsort(-self.dis_adp) - best_adp = self.dis_adp[sortindex[0]] - best_fruit = self.fruits[sortindex[0]] - # 保留种群中适应度最好的个体 - tmp_fruits = self.fruits.copy() - for i in range(int(best_num)): - index = sortindex[i] - self.fruits[i] = tmp_fruits[index] - # self.fruits[0] = best_fruit - - for i in range(int(start_ge), num_total): - # 2opt生成一个新个体 - index = np.random.randint(0,best_num-1) - opt_2_res = self._2opt(self.fruits[index].copy()) - self.fruits[i] = opt_2_res - return best_adp, best_fruit - # 总共的迭代过程，返回最终最好的长度，以及一段路径 - def run(self): - best_fruit = None - best_adp = -math.inf - for i in range(self.iteration): - tmp_adp, tmp_list = self.fly(self.fruits,self.num_total) - if tmp_adp > best_adp: - best_adp = tmp_adp - best_fruit = tmp_list - bestlen = 1.0/best_adp - return self.location[best_fruit],bestlen -import pandas as pd -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) -data = data[:,1:] -result_ = [] -runtimes = 20 -import time -start_time = time.time() -for _ in tqdm.trange(runtimes): - foa = FOA(num_city=data.shape[0],num_total=50,iteration=1000,data=data.copy(),save_path='sdsdsdssd.txt',save_freq=10) - res, length = foa.run() - result_.append(length) -end_time = time.time() -result_ = np.array(result_) - -print('best:{:.2f}\tworst:{:.2f}\tmean:{:.2f}\tstd:{:.2f}\taverage_time:{:.2f}'.format(result_.min(),result_.max(),result_.mean(), result_.std(),(end_time-start_time)/runtimes)) - -# import os -# # print(os.getcwd()) -# import matplotlib.pyplot as plt -# import numpy as np -# from mpl_toolkits.mplot3d import Axes3D -# -# fig = plt.figure() -# ax = Axes3D(fig) -# colors = ('#377eb8', '#ff7f00', '#4daf4a','#aaaaaa') -# def plot(i, data): -# -# x = data[:,0] -# y = data[:,1] -# z = data[:,2] -# ax.scatter(x, y, z,color=colors[i],marker = '+') -# # ax.set_zlabel('Z', fontdict={'size': 15, 'color': 'red'}) -# # ax.set_ylabel('Y', fontdict={'size': 15, 'color': 'red'}) -# # ax.set_xlabel('X', fontdict={'size': 15, 'color': 'red'}) -# ax.plot3D(data[:,0], data[:,1], data[:,2],color = colors[i]) -# result = [] -# for i in range(4): -# data = pd.read_csv('path5_{}.csv'.format(i),header=None) -# data = np.array(data) -# foa = FOA(num_city=data.shape[0],num_total=20,iteration=100,adp_norm=1000,data=data,save_path='sdsdsdssd.txt',save_freq=10) -# res,lenth = foa.run() -# -# res = np.array(res).astype(np.int) -# plot(i,res) -# -# print(lenth) -# # result.append(res) -# # result = np.concatenate(result,axis = 0) -# # print(result.shape) -# # plot(result) -# plt.show() \ No newline at end of file diff --git a/foa_2opt_greedy_multi_best.py b/foa_2opt_greedy_multi_best.py deleted file mode 100755 index 1d6c85e..0000000 --- a/foa_2opt_greedy_multi_best.py +++ /dev/null @@ -1,234 +0,0 @@ -import random -import numpy as np -import math -import pandas as pd - - -class FOA(object): - def __init__(self, num_city, num_total, iteration, adp_norm, data, save_path, save_freq, **kwargs): - self.num_city = num_city - self.num_total = num_total - self.dis_adp = [0 for _ in range(self.num_total)] - self.iteration = iteration - self.adp_norm = adp_norm - self.save_path = save_path - self.save_freq = save_freq - self.location = data - self.dis_mat = self.distance_p2p_mat() - self.best_num = 1 - self.generate_best() - self.generate_fruits() - print('get data,she shape is {}'.format(self.location.shape)) - - def get_data(self, data): - return np.loadtxt(data)[:, 1:] - - # 贪心初始化一条最优路径 - - def generate_best(self, flag=True): - if flag: - initial = [0] - rest = [x for x in range(1, self.num_city)] - initial_path = 0 - while len(rest) != 0: - start = initial[-1] - tmp_min = math.inf - tmp_choose = -1 - for x in rest: - if self.dis_mat[start][x] < tmp_min: - tmp_min = self.dis_mat[start][x] - tmp_choose = x - initial_path += tmp_min - initial.append(tmp_choose) - rest.remove(tmp_choose) - initial_path += self.dis_mat[tmp_choose][0] - return initial, initial_path - else: - initial = [x for x in range(self.num_city)] - initial_path = 0 - for index in range(self.num_city - 1): - initial_path += self.dis_mat[index][index + 1] - initial_path += self.dis_mat[index][0] - return initial, initial_path - - def generate_fruits(self): - best, initial_path = self.generate_best(True) - self.fruits = [best] - for _ in range(self.num_total - 1): - a = np.random.randint(0, self.num_city) - b = np.random.randint(0, self.num_city) - a = min(a, b) - b = max(a, b) - part1 = list(best[:a]) - part2 = list(best[a:b]) - part3 = list(best[b:]) - if len(part2)>=1: - try: - np.random.shuffle(part2) - part2 = list(part2) - except: - import pdb - pdb.set_trace() - res = part1 + part2 + part3 - self.fruits.append(res) - - # 对称矩阵，两个城市之间的距离 支持2d和3d - def distance_p2p_mat(self): - dis_mat = [] - for i in range(self.num_city): - dis_mat_each = [] - for j in range(self.num_city): - tmp = 0 - for k in range(len(self.location[i])): - tmp += pow(self.location[i][k] - self.location[j][k], 2) - tmp = math.sqrt(tmp) - dis_mat_each.append(tmp) - dis_mat.append(dis_mat_each) - return dis_mat - - # 目标函数计算,适应度计算，中间计算。适应度为1/总距离*10000 - def dis_adp_total(self, ): - self.dis_adp = [] - for i in range(self.num_total): - try: - dis = self.dis_mat[self.fruits[i][self.num_city - 1]][self.fruits[i][0]] # 回家 - except: - import pdb - pdb.set_trace() - for j in range(self.num_city - 1): - dis = self.dis_mat[self.fruits[i][j]][self.fruits[i][j + 1]] + dis - dis_adp_each = self.adp_norm / dis - self.dis_adp.append(dis_adp_each) - - def swap_part(self,list1,list2): - index = len(list1) - list = list1 + list2 - list = list[::-1] - return list[:index], list[index:] - - def opt_2(self, x): - city_list = [i for i in range(self.num_city)] - res_list = [] - for i in range(3): - choice = np.random.choice(city_list, 2) - choice.sort() - a, b = list(choice) - part1 = list(x[:a]) - part2 = list(x[a:b]) - part3 = list(x[b:]) - - # 1 - res = part1 + part2[::-1] + part3 - res_list.append(res) - # 2 - list1, list2 = self.swap_part(part1, part3) - res = list1 + part2 + list2 - res_list.append(res) - # 3 - res = list1 + part2[::-1] + list2 - res_list.append(res) - return res_list - - def fly(self): - self.dis_adp_total() - sortindex = np.argsort(self.dis_adp)[::-1] - best_adps = [] - for sub in sortindex[:self.best_num]: - best_adps.append(self.dis_adp[sub]) - best_fruits = [] - for sub in sortindex[:self.best_num]: - best_fruits.append(self.fruits[sub]) - self.fruits = best_fruits - tmp = 1 - while tmp < self.num_total: - randint = np.random.randint(0,self.best_num) - best_fruit = best_fruits[randint] - opt_2_res = self.opt_2(best_fruit.copy()) - self.fruits += opt_2_res - tmp += 3 - self.fruits = self.fruits[:self.num_total] - - return best_adps[0],best_fruit - - def run(self): - print('==================Running the code==================') - BEST_LIST = None - best_adp = -math.inf - cnt = 0 - with open(self.save_path, 'w') as f: - for i in range(1, self.iteration + 1): - max_adp, best_list = self.fly() - cnt += 1 - if max_adp > best_adp: - cnt = 0 - best_adp = max_adp - BEST_LIST = best_list - if cnt > 50: - break - bestlen = self.adp_norm/best_adp - return self.location[BEST_LIST],bestlen -import pandas as pd -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 = read_tsp('data/burma14.tsp') -data = np.array(data) -data = data[:,1:] -for _ in range(10): - foa = FOA(num_city=data.shape[0],num_total=50,iteration=3000,adp_norm=1000,data=data.copy(),save_path='sdsdsdssd.txt',save_freq=10) - res, length = foa.run() - print(length) -# import os -# # print(os.getcwd()) -# import matplotlib.pyplot as plt -# import numpy as np -# from mpl_toolkits.mplot3d import Axes3D -# -# fig = plt.figure() -# ax = Axes3D(fig) -# colors = ('#377eb8', '#ff7f00', '#4daf4a','#aaaaaa') -# def plot(i, data): -# -# x = data[:,0] -# y = data[:,1] -# z = data[:,2] -# ax.scatter(x, y, z,color=colors[i],marker = '+') -# # ax.set_zlabel('Z', fontdict={'size': 15, 'color': 'red'}) -# # ax.set_ylabel('Y', fontdict={'size': 15, 'color': 'red'}) -# # ax.set_xlabel('X', fontdict={'size': 15, 'color': 'red'}) -# ax.plot3D(data[:,0], data[:,1], data[:,2],color = colors[i]) -# result = [] -# for i in range(4): -# data = pd.read_csv('path5_{}.csv'.format(i),header=None) -# data = np.array(data) -# foa = FOA(num_city=data.shape[0],num_total=20,iteration=100,adp_norm=1000,data=data,save_path='sdsdsdssd.txt',save_freq=10) -# res,lenth = foa.run() -# -# res = np.array(res).astype(np.int) -# plot(i,res) -# -# print(lenth) -# # result.append(res) -# # result = np.concatenate(result,axis = 0) -# # print(result.shape) -# # plot(result) -# plt.show() \ No newline at end of file diff --git a/foa_3opt_greedy.py b/foa_3opt_greedy.py deleted file mode 100755 index 7cfb1ec..0000000 --- a/foa_3opt_greedy.py +++ /dev/null @@ -1,269 +0,0 @@ -import random -import numpy as np -import math -import pandas as pd -import tqdm - -class FOA(object): - def __init__(self, num_city, num_total, iteration, data, save_path, save_freq, **kwargs): - self.num_city = num_city - self.num_total = num_total - self.iteration = iteration - self.save_path = save_path - self.save_freq = save_freq - self.location = data - self.dis_mat = self.compute_dis_mat(num_city,self.location) - self.fruits = self.greedy_init(self.dis_mat,num_total,num_city) - # self.fruits = self.random_init(num_total,num_city) - # 贪心初始化种群 - def greedy_init(self,dis_mat,num_total,num_city): - start_index = 0 - result = [[]]*num_total - for i in range(num_total): - rest = [x for x in range(0, num_city)] - # 所有起始点都已经生成了 - if start_index >= num_city: - start_index = np.random.randint(0,num_city) - result[i] = result[start_index].copy() - continue - current = start_index - rest.remove(current) - # 找到一条最近邻路径 - result_one = [current] - while len(rest) != 0: - tmp_min = math.inf - tmp_choose = -1 - for x in rest: - if dis_mat[current][x] < tmp_min: - tmp_min = dis_mat[current][x] - tmp_choose = x - - current = tmp_choose - result_one.append(tmp_choose) - rest.remove(tmp_choose) - result[i] = result_one - start_index += 1 - return result - # def greedy_init_2(self,dis_mat,num_total,num_city): - # start_index = 0 - # result = [[]]*num_total - # rest = [x for x in range(0, num_city)] - # # 所有起始点都已经生成了 - # if start_index >= num_city: - # start_index = np.random.randint(0,num_city) - # result[i] = result[start_index].copy() - # continue - # current = start_index - # rest.remove(current) - # # 找到一条最近邻路径 - # result_one = [current] - # while len(rest) != 0: - # tmp_min = math.inf - # tmp_choose = -1 - # for x in rest: - # if dis_mat[current][x] < tmp_min: - # tmp_min = dis_mat[current][x] - # tmp_choose = x - # - # current = tmp_choose - # result_one.append(tmp_choose) - # rest.remove(tmp_choose) - # # result[i] = result_one - # result[0] = result_one - # start_index += 1 - # return result - # 随机初始化种群 - def random_init(self,num_total,num_city): - tmp = [x for x in range(num_city)] - result = [[]]*num_total - for i in range(num_total): - random.shuffle(tmp) - result[i] = tmp - return result - # 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]) ** 2for 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] - assert a!=b - result = dis_mat[a][b] - for i in range(len(path) - 1): - a = path[i] - b = path[i+1] - assert a!= b - result += dis_mat[a][b] - return result - # 计算种群适应度 - def compute_adp(self,fruits): - adp = [] - for fruit in fruits: - length = self.compute_pathlen(fruit,self.dis_mat) - adp.append(1.0/length) - return np.array(adp) - # 对序列做2opt操作 - def _3opt(self, x): - city_list = [i for i in range(self.num_city)] - choice = np.random.choice(city_list, 2) - choice.sort() - i, j = choice - a = x[:i] - b = x[i:j] - c = x[j:] - tmp_best = [] - tmp_best_adp = [] - # param1 - # tmp = a+b+c - # tmp_adp = 1./self.compute_pathlen(tmp, self.dis_mat) - # tmp_best = [a + b + c, ] - # tmp_best_adp = [tmp_adp] - # param2 - tmp = a+b+c[::-1] - tmp_adp = 1./self.compute_pathlen(tmp, self.dis_mat) - tmp_best.append(tmp) - tmp_best_adp.append(tmp_adp) - # param3 - tmp = a+c[::-1]+b[::-1] - tmp_best.append(tmp) - tmp_best_adp.append(1./self.compute_pathlen(tmp, self.dis_mat)) - # param4 - tmp = a+b[::-1]+c - tmp_best.append(tmp) - tmp_best_adp.append(1./self.compute_pathlen(tmp,self.dis_mat)) - # param5 - tmp = a+c+b[::-1] - tmp_best.append(tmp) - tmp_best_adp.append(1./self.compute_pathlen(tmp, self.dis_mat)) - # param6 - tmp = a+c[::-1]+b - tmp_best.append(tmp) - tmp_best_adp.append(1./self.compute_pathlen(tmp, self.dis_mat)) - # param7 - tmp = a+b[::-1]+c[::-1] - tmp_best.append(tmp) - tmp_best_adp.append(1./self.compute_pathlen(tmp, self.dis_mat)) - # param8 - tmp = a+c+b - tmp_best.append(tmp) - tmp_best_adp.append(1./self.compute_pathlen(tmp, self.dis_mat)) - - max_adp = max(tmp_best_adp) - max_index = tmp_best_adp.index(max_adp) - result = tmp_best[max_index] - return result - # 迭代一次，返回适应度最高的个体和他的序列。 - def fly(self,fruits,num_total): - # 选择适应度最高的个体 - self.dis_adp = self.compute_adp(self.fruits) - # 从高到底适应度排序 - sortindex = np.argsort(-self.dis_adp) - best_adp = self.dis_adp[sortindex[0]] - best_fruit = self.fruits[sortindex[0]] - # 保留种群中适应度最好的个体 - self.fruits[0] = best_fruit - for i in range(1, num_total): - # 2opt生成一个新个体 - - opt_2_res = self._3opt(best_fruit.copy()) - self.fruits[i] = opt_2_res - return best_adp, best_fruit - # 总共的迭代过程，返回最终最好的长度，以及一段路径 - def run(self): - best_fruit = None - best_adp = -math.inf - best_list = [] - for i in range(self.iteration): - tmp_adp, tmp_list = self.fly(self.fruits,self.num_total) - if tmp_adp > best_adp: - best_adp = tmp_adp - best_fruit = tmp_list - # best_list = [] - # best_list.append(best_adp) - # if len(best_list) == 50: - # break - bestlen = 1.0/best_adp - return self.location[best_fruit],bestlen -import pandas as pd -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) -data = data[:,1:] -result_ = [] -runtimes = 20 -import time -start_time = time.time() -for _ in tqdm.trange(runtimes): - foa = FOA(num_city=data.shape[0],num_total=50,iteration=1000,data=data.copy(),save_path='sdsdsdssd.txt',save_freq=10) - res, length = foa.run() - result_.append(length) -end_time = time.time() -result_ = np.array(result_) - -print('best:{:.2f}\tworst:{:.2f}\tmean:{:.2f}\tstd:{:.2f}\taverage_time:{:.2f}'.format(result_.min(),result_.max(),result_.mean(), result_.std(),(end_time-start_time)/runtimes)) - -# import os -# # print(os.getcwd()) -# import matplotlib.pyplot as plt -# import numpy as np -# from mpl_toolkits.mplot3d import Axes3D -# -# fig = plt.figure() -# ax = Axes3D(fig) -# colors = ('#377eb8', '#ff7f00', '#4daf4a','#aaaaaa') -# def plot(i, data): -# -# x = data[:,0] -# y = data[:,1] -# z = data[:,2] -# ax.scatter(x, y, z,color=colors[i],marker = '+') -# # ax.set_zlabel('Z', fontdict={'size': 15, 'color': 'red'}) -# # ax.set_ylabel('Y', fontdict={'size': 15, 'color': 'red'}) -# # ax.set_xlabel('X', fontdict={'size': 15, 'color': 'red'}) -# ax.plot3D(data[:,0], data[:,1], data[:,2],color = colors[i]) -# result = [] -# for i in range(4): -# data = pd.read_csv('path5_{}.csv'.format(i),header=None) -# data = np.array(data) -# foa = FOA(num_city=data.shape[0],num_total=20,iteration=100,adp_norm=1000,data=data,save_path='sdsdsdssd.txt',save_freq=10) -# res,lenth = foa.run() -# -# res = np.array(res).astype(np.int) -# plot(i,res) -# -# print(lenth) -# # result.append(res) -# # result = np.concatenate(result,axis = 0) -# # print(result.shape) -# # plot(result) -# plt.show() \ No newline at end of file diff --git a/foa_3opt_greedy_bak.py b/foa_3opt_greedy_bak.py deleted file mode 100755 index 86a9577..0000000 --- a/foa_3opt_greedy_bak.py +++ /dev/null @@ -1,225 +0,0 @@ -import random -import numpy as np -import math -import pandas as pd -import time - -class FOA(object): - def __init__(self, num_city, num_total, iteration, adp_norm, data, save_path, save_freq, **kwargs): - self.num_city = num_city - self.num_total = num_total - self.dis_adp = [0 for _ in range(self.num_total)] - self.iteration = iteration - self.adp_norm = adp_norm - self.save_path = save_path - self.save_freq = save_freq - self.location = data - self.dis_mat = self.distance_p2p_mat() - self.generate_best() - self.generate_fruits() - print('get data,she shape is {}'.format(self.location.shape)) - - def get_data(self, data): - return np.loadtxt(data)[:, 1:] - - # 贪心初始化一条最优路径 - def generate_best(self): - initial = [0] - rest = [x for x in range(1, self.num_city)] - initial_path = 0 - while len(rest) != 0: - start = initial[-1] - tmp_min = math.inf - tmp_choose = -1 - for x in rest: - if self.dis_mat[start][x] < tmp_min: - tmp_min = self.dis_mat[start][x] - tmp_choose = x - initial_path += tmp_min - initial.append(tmp_choose) - rest.remove(tmp_choose) - initial_path += self.dis_mat[tmp_choose][0] - return initial, initial_path - - def generate_fruits(self): - best, initial_path = self.generate_best() - self.fruits = [best] - for _ in range(self.num_total - 1): - a = np.random.randint(0, self.num_city) - b = np.random.randint(0, self.num_city) - a = min(a, b) - b = max(a, b) - part1 = list(best[:a]) - part2 = list(best[a:b]) - part3 = list(best[b:]) - if len(part2)>=1: - try: - np.random.shuffle(part2) - part2 = list(part2) - except: - import pdb - pdb.set_trace() - res = part1 + part2 + part3 - self.fruits.append(res) - - # 对称矩阵，两个城市之间的距离 支持2d和3d - def distance_p2p_mat(self): - dis_mat = [] - for i in range(self.num_city): - dis_mat_each = [] - for j in range(self.num_city): - tmp = 0 - for k in range(len(self.location[i])): - tmp += pow(self.location[i][k] - self.location[j][k], 2) - tmp = math.sqrt(tmp) - dis_mat_each.append(tmp) - dis_mat.append(dis_mat_each) - return dis_mat - - # 目标函数计算,适应度计算，中间计算。适应度为1/总距离*10000 - def dis_adp_total(self, ): - self.dis_adp = [] - for i in range(self.num_total): - try: - dis = self.dis_mat[self.fruits[i][self.num_city - 1]][self.fruits[i][0]] # 回家 - except: - import pdb - pdb.set_trace() - for j in range(self.num_city - 1): - dis = self.dis_mat[self.fruits[i][j]][self.fruits[i][j + 1]] + dis - dis_adp_each = self.adp_norm / dis - self.dis_adp.append(dis_adp_each) - - def swap_part(self,list1,list2): - index = len(list1) - list = list1 + list2 - list = list[::-1] - return list[:index], list[index:] - - def fly(self): - self.dis_adp_total() - sortindex = np.argsort(self.dis_adp)[::-1] - best_adp = self.dis_adp[sortindex[0]] - best_fruit = self.fruits[sortindex[0]] - self.fruits = [best_fruit] - city_list = [i for i in range(self.num_city)] - cnt = 0 - while cnt < self.num_total-1: - choice = np.random.choice(city_list, 3) - choice.sort() - a, b, c = list(choice) - part1 = list(best_fruit[:a]) - part2 = list(best_fruit[a:b]) - part3 = list(best_fruit[b:c]) - part4 = list(best_fruit[c:]) - # 1 - res = part1+part2[::-1]+part3+part4 - assert len(res) == 70,print(len) - self.fruits.append(res) - # 2 - res = part1 + part2 + part3[::-1] + part4 - assert len(res) == 70 - self.fruits.append(res) - # 3 - list1, list2 = self.swap_part(part1, part4) - res = list1 + part2 + part3 + list2 - assert len(res) == 70 - self.fruits.append(res) - # 4 - res = part1 + part2[::-1] + part3[::-1] + part4 - assert len(res) == 70 - self.fruits.append(res) - # 5 - res = list1 + part2[::-1] + part3 + list2 - assert len(res) == 70 - self.fruits.append(res) - # 6 - res = list1 + part2 + part3[::-1] + list2 - assert len(res) == 70 - self.fruits.append(res) - # # 7 - # res = part1 + part3 + part2 + part4 - # self.fruits.append(res) - # # 8 - # res = part2 + part1 + part3 + part4 - # self.fruits.append(res) - cnt += 6 - self.fruits = self.fruits[:self.num_total] - - return best_adp,best_fruit - - def run(self): - print('==================Running the code==================') - BEST_LIST = None - best_adp = -math.inf - with open(self.save_path, 'w') as f: - for i in range(1, self.iteration + 1): - aaaaa = time.time() - max_adp, best_list = self.fly() - if max_adp > best_adp: - best_adp = max_adp - BEST_LIST = best_list - bbbb = time.time() - print(bbbb - aaaaa) - bestlen = self.adp_norm/best_adp - return self.location[BEST_LIST],bestlen -import pandas as pd -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(' ') - tmpline = [] - for x in line: - if x == '': - continue - else: - tmpline.append(int(x)) - tmp.append(tmpline) - data = tmp - return data -data = read_tsp('data/st70.tsp') -data = np.array(data) -data = data[:,1:] -for _ in range(10): - foa = FOA(num_city=data.shape[0],num_total=50,iteration=1000,adp_norm=1000,data=data.copy().astype(np.int),save_path='sdsdsdssd.txt',save_freq=10) - res, length = foa.run() - print(length) -# import os -# # print(os.getcwd()) -# import matplotlib.pyplot as plt -# import numpy as np -# from mpl_toolkits.mplot3d import Axes3D -# -# fig = plt.figure() -# ax = Axes3D(fig) -# colors = ('#377eb8', '#ff7f00', '#4daf4a','#aaaaaa') -# def plot(i, data): -# -# x = data[:,0] -# y = data[:,1] -# z = data[:,2] -# ax.scatter(x, y, z,color=colors[i],marker = '+') -# # ax.set_zlabel('Z', fontdict={'size': 15, 'color': 'red'}) -# # ax.set_ylabel('Y', fontdict={'size': 15, 'color': 'red'}) -# # ax.set_xlabel('X', fontdict={'size': 15, 'color': 'red'}) -# ax.plot3D(data[:,0], data[:,1], data[:,2],color = colors[i]) -# result = [] -# for i in range(4): -# data = pd.read_csv('path5_{}.csv'.format(i),header=None) -# data = np.array(data) -# foa = FOA(num_city=data.shape[0],num_total=20,iteration=100,adp_norm=1000,data=data,save_path='sdsdsdssd.txt',save_freq=10) -# res,lenth = foa.run() -# -# res = np.array(res).astype(np.int) -# plot(i,res) -# -# print(lenth) -# # result.append(res) -# # result = np.concatenate(result,axis = 0) -# # print(result.shape) -# # plot(result) -# plt.show() \ No newline at end of file diff --git a/foa_greedy_single_GA.py b/foa_greedy_single_GA.py deleted file mode 100755 index aad7fa8..0000000 --- a/foa_greedy_single_GA.py +++ /dev/null @@ -1,297 +0,0 @@ -import random -import numpy as np -import math -import pandas as pd -import tqdm - - -class FOA(object): - def __init__(self, num_city, num_total, iteration, data, save_path, save_freq, **kwargs): - self.num_city = num_city - self.num_total = num_total - self.iteration = iteration - self.save_path = save_path - self.save_freq = save_freq - self.location = data - self.dis_mat = self.compute_dis_mat(num_city, self.location) - # self.fruits = self.greedy_init(self.dis_mat, num_total, num_city) - self.fruits = self.random_init(num_total,num_city) - - # 贪心初始化种群 - def greedy_init(self, dis_mat, num_total, num_city): - start_index = 0 - result = [] - for i in range(num_total): - rest = [x for x in range(0, num_city)] - # 所有起始点都已经生成了 - if start_index >= num_city: - start_index = np.random.randint(0, num_city) - result.append(result[start_index].copy()) - continue - current = start_index - rest.remove(current) - # 找到一条最近邻路径 - result_one = [current] - while len(rest) != 0: - tmp_min = math.inf - tmp_choose = -1 - for x in rest: - if dis_mat[current][x] < tmp_min: - tmp_min = dis_mat[current][x] - tmp_choose = x - - current = tmp_choose - result_one.append(tmp_choose) - rest.remove(tmp_choose) - result.append(result_one) - start_index += 1 - return result - - # def greedy_init_2(self,dis_mat,num_total,num_city): - # start_index = 0 - # result = [[]]*num_total - # rest = [x for x in range(0, num_city)] - # # 所有起始点都已经生成了 - # if start_index >= num_city: - # start_index = np.random.randint(0,num_city) - # result[i] = result[start_index].copy() - # continue - # current = start_index - # rest.remove(current) - # # 找到一条最近邻路径 - # result_one = [current] - # while len(rest) != 0: - # tmp_min = math.inf - # tmp_choose = -1 - # for x in rest: - # if dis_mat[current][x] < tmp_min: - # tmp_min = dis_mat[current][x] - # tmp_choose = x - # - # current = tmp_choose - # result_one.append(tmp_choose) - # rest.remove(tmp_choose) - # # result[i] = result_one - # result[0] = result_one - # start_index += 1 - # return result - # 随机初始化种群 - def random_init(self, num_total, num_city): - tmp = [x for x in range(num_city)] - result = [] - for i in range(num_total): - random.shuffle(tmp) - result.append(tmp.copy()) - return result - - # 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] - assert a != b - result = dis_mat[a][b] - for i in range(len(path) - 1): - a = path[i] - b = path[i + 1] - assert a != b - result += dis_mat[a][b] - return result - - # 计算种群适应度 - def compute_adp(self, fruits): - adp = [] - for fruit in fruits: - length = self.compute_pathlen(fruit, self.dis_mat) - adp.append(1.0 / length) - return np.array(adp) - - # 对序列做2opt操作 - def single_GA(self, best, x): - # 1.翻转法，根据序列的差异长度选择反转长度 - tmp = np.array(best) - np.array(x) - l = np.where(tmp != 0) - l = len(l[0]) - # top = max(self.num_city - l,3) - top = 5 - print(l) - a = np.random.randint(top) - t = x[a:a+l] - return x[:a] + t[::-1]+x[a+l:] - - - # def _3opt(self, x): - # city_list = [i for i in range(self.num_city)] - # choice = np.random.choice(city_list, 2) - # choice.sort() - # i, j = choice - # a = x[:i] - # b = x[i:j] - # c = x[j:] - # tmp_best = [] - # tmp_best_adp = [] - # # param1 - # # tmp = a+b+c - # # tmp_adp = 1./self.compute_pathlen(tmp, self.dis_mat) - # # tmp_best = [a + b + c, ] - # # tmp_best_adp = [tmp_adp] - # # param2 - # tmp = a + b + c[::-1] - # tmp_adp = 1. / self.compute_pathlen(tmp, self.dis_mat) - # tmp_best.append(tmp) - # tmp_best_adp.append(tmp_adp) - # # param3 - # tmp = a + c[::-1] + b[::-1] - # tmp_best.append(tmp) - # tmp_best_adp.append(1. / self.compute_pathlen(tmp, self.dis_mat)) - # # param4 - # tmp = a + b[::-1] + c - # tmp_best.append(tmp) - # tmp_best_adp.append(1. / self.compute_pathlen(tmp, self.dis_mat)) - # # param5 - # tmp = a + c + b[::-1] - # tmp_best.append(tmp) - # tmp_best_adp.append(1. / self.compute_pathlen(tmp, self.dis_mat)) - # # param6 - # tmp = a + c[::-1] + b - # tmp_best.append(tmp) - # tmp_best_adp.append(1. / self.compute_pathlen(tmp, self.dis_mat)) - # # param7 - # tmp = a + b[::-1] + c[::-1] - # tmp_best.append(tmp) - # tmp_best_adp.append(1. / self.compute_pathlen(tmp, self.dis_mat)) - # # param8 - # tmp = a + c + b - # tmp_best.append(tmp) - # tmp_best_adp.append(1. / self.compute_pathlen(tmp, self.dis_mat)) - # - # max_adp = max(tmp_best_adp) - # max_index = tmp_best_adp.index(max_adp) - # result = tmp_best[max_index] - # return result - - # 迭代一次，返回适应度最高的个体和他的序列。 - def fly(self, fruits, num_total): - # 选择适应度最高的个体 - self.dis_adp = self.compute_adp(self.fruits) - # 从高到底适应度排序 - sortindex = np.argsort(-self.dis_adp) - best_adp = self.dis_adp[sortindex[0]] - best_fruit = self.fruits[sortindex[0]] - # 保留种群中适应度最好的个体 - self.fruits[0] = best_fruit - for i in range(1, num_total): - # 2opt生成一个新个体 - x = self.fruits[i] - opt_2_res = self.single_GA(best_fruit,x) - self.fruits[i] = opt_2_res - return best_adp, best_fruit - - # 总共的迭代过程，返回最终最好的长度，以及一段路径 - def run(self): - best_fruit = None - best_adp = -math.inf - for i in range(self.iteration): - tmp_adp, tmp_list = self.fly(self.fruits, self.num_total) - if tmp_adp > best_adp: - print('wwww') - best_adp = tmp_adp - best_fruit = tmp_list - bestlen = 1.0 / best_adp - return self.location[best_fruit], bestlen - - -import pandas as pd - - -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) -data = data[:, 1:] -result_ = [] -runtimes = 1 -import time - -start_time = time.time() -for _ in tqdm.trange(runtimes): - foa = FOA(num_city=data.shape[0], num_total=50, iteration=1000, data=data.copy(), save_path='sdsdsdssd.txt', - save_freq=10) - res, length = foa.run() - result_.append(length) -end_time = time.time() -result_ = np.array(result_) - -print('best:{:.2f}\tworst:{:.2f}\tmean:{:.2f}\tstd:{:.2f}\taverage_time:{:.2f}'.format(result_.min(), result_.max(), - result_.mean(), result_.std(), ( - end_time - start_time) / runtimes)) - -# import os -# # print(os.getcwd()) -# import matplotlib.pyplot as plt -# import numpy as np -# from mpl_toolkits.mplot3d import Axes3D -# -# fig = plt.figure() -# ax = Axes3D(fig) -# colors = ('#377eb8', '#ff7f00', '#4daf4a','#aaaaaa') -# def plot(i, data): -# -# x = data[:,0] -# y = data[:,1] -# z = data[:,2] -# ax.scatter(x, y, z,color=colors[i],marker = '+') -# # ax.set_zlabel('Z', fontdict={'size': 15, 'color': 'red'}) -# # ax.set_ylabel('Y', fontdict={'size': 15, 'color': 'red'}) -# # ax.set_xlabel('X', fontdict={'size': 15, 'color': 'red'}) -# ax.plot3D(data[:,0], data[:,1], data[:,2],color = colors[i]) -# result = [] -# for i in range(4): -# data = pd.read_csv('path5_{}.csv'.format(i),header=None) -# data = np.array(data) -# foa = FOA(num_city=data.shape[0],num_total=20,iteration=100,adp_norm=1000,data=data,save_path='sdsdsdssd.txt',save_freq=10) -# res,lenth = foa.run() -# -# res = np.array(res).astype(np.int) -# plot(i,res) -# -# print(lenth) -# # result.append(res) -# # result = np.concatenate(result,axis = 0) -# # print(result.shape) -# # plot(result) -# plt.show() diff --git a/foa_shuffle_part.py b/foa_shuffle_part.py deleted file mode 100755 index 8a397ab..0000000 --- a/foa_shuffle_part.py +++ /dev/null @@ -1,235 +0,0 @@ -import random -import numpy as np -import math -import pandas as pd - - -class FOA(object): - def __init__(self, num_city, num_total, iteration, adp_norm, data, save_path, save_freq, **kwargs): - self.num_city = num_city - self.num_total = num_total - self.dis_adp = [0 for _ in range(self.num_total)] - self.iteration = iteration - self.adp_norm = adp_norm - self.save_path = save_path - self.save_freq = save_freq - self.location = data - self.dis_mat = self.distance_p2p_mat() - self.generate_best() - self.generate_fruits() - print('get data,she shape is {}'.format(self.location.shape)) - - def get_data(self, data): - return np.loadtxt(data)[:, 1:] - - # 贪心初始化一条最优路径 - - def generate_best(self, flag=True): - if flag: - initial = [0] - rest = [x for x in range(1, self.num_city)] - initial_path = 0 - while len(rest) != 0: - start = initial[-1] - tmp_min = math.inf - tmp_choose = -1 - for x in rest: - if self.dis_mat[start][x] < tmp_min: - tmp_min = self.dis_mat[start][x] - tmp_choose = x - initial_path += tmp_min - initial.append(tmp_choose) - rest.remove(tmp_choose) - initial_path += self.dis_mat[tmp_choose][0] - return initial, initial_path - else: - initial = [x for x in range(self.num_city)] - initial_path = 0 - for index in range(self.num_city - 1): - initial_path += self.dis_mat[index][index + 1] - initial_path += self.dis_mat[index][0] - return initial, initial_path - - def generate_fruits(self): - best, initial_path = self.generate_best(True) - self.fruits = [best] - for _ in range(self.num_total - 1): - a = np.random.randint(0, self.num_city) - b = np.random.randint(0, self.num_city) - a = min(a, b) - b = max(a, b) - part1 = list(best[:a]) - part2 = list(best[a:b]) - part3 = list(best[b:]) - if len(part2)>=1: - try: - np.random.shuffle(part2) - part2 = list(part2) - except: - import pdb - pdb.set_trace() - res = part1 + part2 + part3 - self.fruits.append(res) - - # 对称矩阵，两个城市之间的距离 支持2d和3d - def distance_p2p_mat(self): - dis_mat = [] - for i in range(self.num_city): - dis_mat_each = [] - for j in range(self.num_city): - tmp = 0 - for k in range(len(self.location[i])): - tmp += pow(self.location[i][k] - self.location[j][k], 2) - tmp = math.sqrt(tmp) - dis_mat_each.append(tmp) - dis_mat.append(dis_mat_each) - return dis_mat - - # 目标函数计算,适应度计算，中间计算。适应度为1/总距离*10000 - def dis_adp_total(self, ): - self.dis_adp = [] - for i in range(self.num_total): - try: - dis = self.dis_mat[self.fruits[i][self.num_city - 1]][self.fruits[i][0]] # 回家 - except: - import pdb - pdb.set_trace() - for j in range(self.num_city - 1): - dis = self.dis_mat[self.fruits[i][j]][self.fruits[i][j + 1]] + dis - dis_adp_each = self.adp_norm / dis - self.dis_adp.append(dis_adp_each) - - def swap_part(self,list1,list2): - index = len(list1) - list = list1 + list2 - list = list[::-1] - return list[:index], list[index:] - - def opt_2(self, x): - city_list = [i for i in range(self.num_city)] - choice = np.random.choice(city_list, 2) - choice.sort() - res_list = [] - a, b = list(choice) - part1 = list(x[:a]) - part2 = list(x[a:b]) - part3 = list(x[b:]) - - # 1 - res = part1 + part2[::-1] + part3 - res_list.append(res) - # 2 - list1, list2 = self.swap_part(part1, part3) - res = list1 + part2 + list2 - res_list.append(res) - # 3 - res = list1 + part2[::-1] + list2 - res_list.append(res) - return res_list - - def shuffle_part(self,data): - x = np.random.randint(0,len(data)) - y = np.random.randint(0,len(data)) - start = min(x,y) - end = max(y,x) - # x = np.random.randint(0,len(data)-10) - # y = x+10 - part1 = data[:start] - part2 = data[start:end] - part3 = data[end:] - # np.random.shuffle(part2) - part2 = part2[::-1] - res_list = [part1+part2+part3] - return res_list - def fly(self): - self.dis_adp_total() - sortindex = np.argsort(self.dis_adp)[::-1] - best_adp = self.dis_adp[sortindex[0]] - best_fruit = self.fruits[sortindex[0]] - self.fruits = [best_fruit] - tmp = 1 - while tmp < self.num_total: - opt_2_res = self.shuffle_part(best_fruit.copy()) - self.fruits += opt_2_res - tmp += len(opt_2_res) - self.fruits = self.fruits[:self.num_total] - - return best_adp,best_fruit - - def run(self): - print('==================Running the code==================') - BEST_LIST = None - best_adp = -math.inf - with open(self.save_path, 'w') as f: - for i in range(1, self.iteration + 1): - max_adp, best_list = self.fly() - if max_adp > best_adp: - best_adp = max_adp - BEST_LIST = best_list - bestlen = self.adp_norm/best_adp - return self.location[BEST_LIST],bestlen -import pandas as pd -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 = read_tsp('data/burma14.tsp') -data = np.array(data) -data = data[:,1:] -for _ in range(10): - foa = FOA(num_city=data.shape[0],num_total=50,iteration=1000,adp_norm=1000,data=data.copy(),save_path='sdsdsdssd.txt',save_freq=10) - res, length = foa.run() - print(length) -# import os -# # print(os.getcwd()) -# import matplotlib.pyplot as plt -# import numpy as np -# from mpl_toolkits.mplot3d import Axes3D -# -# fig = plt.figure() -# ax = Axes3D(fig) -# colors = ('#377eb8', '#ff7f00', '#4daf4a','#aaaaaa') -# def plot(i, data): -# -# x = data[:,0] -# y = data[:,1] -# z = data[:,2] -# ax.scatter(x, y, z,color=colors[i],marker = '+') -# # ax.set_zlabel('Z', fontdict={'size': 15, 'color': 'red'}) -# # ax.set_ylabel('Y', fontdict={'size': 15, 'color': 'red'}) -# # ax.set_xlabel('X', fontdict={'size': 15, 'color': 'red'}) -# ax.plot3D(data[:,0], data[:,1], data[:,2],color = colors[i]) -# result = [] -# for i in range(4): -# data = pd.read_csv('path5_{}.csv'.format(i),header=None) -# data = np.array(data) -# foa = FOA(num_city=data.shape[0],num_total=20,iteration=100,adp_norm=1000,data=data,save_path='sdsdsdssd.txt',save_freq=10) -# res,lenth = foa.run() -# -# res = np.array(res).astype(np.int) -# plot(i,res) -# -# print(lenth) -# # result.append(res) -# # result = np.concatenate(result,axis = 0) -# # print(result.shape) -# # plot(result) -# plt.show() \ No newline at end of file diff --git a/foa_shufllepart_greedy.py b/foa_shufllepart_greedy.py deleted file mode 100755 index c97def1..0000000 --- a/foa_shufllepart_greedy.py +++ /dev/null @@ -1,187 +0,0 @@ -import random -import numpy as np -import math -import pandas as pd - - -class FOA(object): - def __init__(self, num_city, num_total, iteration, adp_norm, data, save_path, save_freq, **kwargs): - self.num_city = num_city - self.num_total = num_total - self.dis_adp = [0 for _ in range(self.num_total)] - self.iteration = iteration - self.adp_norm = adp_norm - self.save_path = save_path - self.save_freq = save_freq - self.location = data - self.dis_mat = self.distance_p2p_mat() - self.generate_best() - self.generate_fruits() - print('get data,she shape is {}'.format(self.location.shape)) - - def get_data(self, data): - return np.loadtxt(data)[:, 1:] - - # 贪心初始化一条最优路径 - def generate_best(self): - initial = [0] - rest = [x for x in range(1, self.num_city)] - initial_path = 0 - while len(rest) != 0: - start = initial[-1] - tmp_min = math.inf - tmp_choose = -1 - for x in rest: - if self.dis_mat[start][x] < tmp_min: - tmp_min = self.dis_mat[start][x] - tmp_choose = x - initial_path += tmp_min - initial.append(tmp_choose) - rest.remove(tmp_choose) - initial_path += self.dis_mat[tmp_choose][0] - return initial, initial_path - - def generate_fruits(self): - best, initial_path = self.generate_best() - self.fruits = [best] - for _ in range(self.num_total - 1): - a = np.random.randint(0, self.num_city) - b = np.random.randint(0, self.num_city) - a = min(a, b) - b = max(a, b) - part1 = list(best[:a]) - part2 = list(best[a:b]) - part3 = list(best[b:]) - if len(part2) >= 1: - try: - np.random.shuffle(part2) - part2 = list(part2) - except: - import pdb - pdb.set_trace() - res = part1 + part2 + part3 - self.fruits.append(res) - - # 对称矩阵，两个城市之间的距离 支持2d和3d - def distance_p2p_mat(self): - dis_mat = [] - for i in range(self.num_city): - dis_mat_each = [] - for j in range(self.num_city): - tmp = 0 - for k in range(len(self.location[i])): - tmp += pow(self.location[i][k] - self.location[j][k], 2) - tmp = math.sqrt(tmp) - dis_mat_each.append(tmp) - dis_mat.append(dis_mat_each) - return dis_mat - - # 目标函数计算,适应度计算，中间计算。适应度为1/总距离*10000 - def dis_adp_total(self, ): - self.dis_adp = [] - for i in range(self.num_total): - dis = self.dis_mat[self.fruits[i][self.num_city - 1]][self.fruits[i][0]] # 回家 - for j in range(self.num_city - 1): - dis = self.dis_mat[self.fruits[i][j]][self.fruits[i][j + 1]] + dis - dis_adp_each = self.adp_norm / dis - self.dis_adp.append(dis_adp_each) - - def fly(self): - self.dis_adp_total() - sortindex = np.argsort(self.dis_adp)[::-1] - best_adp = self.dis_adp[sortindex[0]] - best_fruit = self.fruits[sortindex[0]] - self.fruits = [best_fruit] - for _ in range(self.num_total - 1): - a = np.random.randint(0, self.num_city) - b = np.random.randint(0, self.num_city) - a = min(a, b) - b = max(a, b) - part1 = list(best_fruit[:a]) - part2 = list(best_fruit[a:b]) - part3 = list(best_fruit[b:]) - if len(part2) >= 1: - np.random.shuffle(part2) - part2 = list(part2) - res = part1 + part2 + part3 - self.fruits.append(res) - return best_adp, best_fruit - - def run(self): - print('==================Running the code==================') - BEST_LIST = None - best_adp = -math.inf - with open(self.save_path, 'w') as f: - for i in range(1, self.iteration + 1): - max_adp, best_list = self.fly() - if max_adp > best_adp: - best_adp = max_adp - BEST_LIST = best_list - bestlen = self.adp_norm / best_adp - return self.location[BEST_LIST], bestlen - - -import pandas as pd - - -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(' ') - tmpline = [] - for x in line: - if x == '': - continue - else: - tmpline.append(int(x)) - tmp.append(tmpline) - data = tmp - return data - - -data = read_tsp('data/st70.tsp') -data = np.array(data) -data = data[:, 1:] -foa = FOA(num_city=data.shape[0], num_total=50, iteration=1000, adp_norm=1000, data=data, save_path='sdsdsdssd.txt', - save_freq=10) -res, len = foa.run() -print(len) -# import os -# # print(os.getcwd()) -# import matplotlib.pyplot as plt -# import numpy as np -# from mpl_toolkits.mplot3d import Axes3D -# -# fig = plt.figure() -# ax = Axes3D(fig) -# colors = ('#377eb8', '#ff7f00', '#4daf4a','#aaaaaa') -# def plot(i, data): -# -# x = data[:,0] -# y = data[:,1] -# z = data[:,2] -# ax.scatter(x, y, z,color=colors[i],marker = '+') -# # ax.set_zlabel('Z', fontdict={'size': 15, 'color': 'red'}) -# # ax.set_ylabel('Y', fontdict={'size': 15, 'color': 'red'}) -# # ax.set_xlabel('X', fontdict={'size': 15, 'color': 'red'}) -# ax.plot3D(data[:,0], data[:,1], data[:,2],color = colors[i]) -# result = [] -# for i in range(4): -# data = pd.read_csv('path5_{}.csv'.format(i),header=None) -# data = 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