# mirrors / guofei9987 / scikit-opt 大约 18 小时 前同步成功

### version 0.5.4

 ... ... @@ -401,12 +401,12 @@ More: Plot the animation: ## 5. ACA (Ant Colony Algorithm) for tsp -> Demo code: [examples/demo_aca_tsp.py#s2](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_aca_tsp.py#L23) -> Demo code: [examples/demo_aca_tsp.py#s2](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_aca_tsp.py#L17) ```python from sko.ACA import ACA_TSP aca = ACA_TSP(func=cal_total_distance, n_dim=8, size_pop=10, max_iter=20, aca = ACA_TSP(func=cal_total_distance, n_dim=num_points, size_pop=50, max_iter=200, distance_matrix=distance_matrix) best_x, best_y = aca.run() ... ...
 ... ... @@ -381,12 +381,12 @@ More: Plot the animation: ## 5. ACA (Ant Colony Algorithm) for tsp -> Demo code: [examples/demo_aca_tsp.py#s2](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_aca_tsp.py#L23) -> Demo code: [examples/demo_aca_tsp.py#s2](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_aca_tsp.py#L17) ```python from sko.ACA import ACA_TSP aca = ACA_TSP(func=cal_total_distance, n_dim=8, size_pop=10, max_iter=20, aca = ACA_TSP(func=cal_total_distance, n_dim=num_points, size_pop=50, max_iter=200, distance_matrix=distance_matrix) best_x, best_y = aca.run() ... ...
 ... ... @@ -12,6 +12,8 @@ 需要从py文件中解析出： 1. # %% 做断点后赋予index值，然后插入readme ''' import os import sys import re ... ... @@ -93,3 +95,5 @@ for i in docs: docs_new = make_doc(origin_file=i) with open(i, encoding='utf-8', mode="w") as f: f.writelines(docs_new) sys.exit()
 ... ... @@ -350,12 +350,12 @@ print(best_points, best_distance, cal_total_distance(best_points)) ## 5. 蚁群算法 蚁群算法(ACA, Ant Colony Algorithm)解决TSP问题 -> Demo code: [examples/demo_aca_tsp.py#s2](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_aca_tsp.py#L23) -> Demo code: [examples/demo_aca_tsp.py#s2](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_aca_tsp.py#L17) ```python from sko.ACA import ACA_TSP aca = ACA_TSP(func=cal_total_distance, n_dim=8, size_pop=10, max_iter=20, aca = ACA_TSP(func=cal_total_distance, n_dim=num_points, size_pop=50, max_iter=200, distance_matrix=distance_matrix) best_x, best_y = aca.run() ... ...
 ... ... @@ -3,7 +3,6 @@ from scipy import spatial import pandas as pd import matplotlib.pyplot as plt np.random.seed(6) num_points = 25 points_coordinate = np.random.rand(num_points, 2) # generate coordinate of points ... ... @@ -25,8 +24,9 @@ aca = ACA_TSP(func=cal_total_distance, n_dim=num_points, best_x, best_y = aca.run() # %% Plot fig, ax = plt.subplots(1, 1) fig, ax = plt.subplots(1, 2) best_points_ = np.concatenate([best_x, [best_x[0]]]) best_points_coordinate = points_coordinate[best_points_, :] ax.plot(best_points_coordinate[:, 0], best_points_coordinate[:, 1], 'o-r') ax[0].plot(best_points_coordinate[:, 0], best_points_coordinate[:, 1], 'o-r') pd.DataFrame(aca.y_best_history).cummin().plot(ax=ax[1]) plt.show()
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