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how to use Python3 to solve salesman problem, Shortest Path through Points,
Shortest Path.dyn (26.4 KB)
I use eploy Python genetic algorithm library as shown in the below link
import sys
import clr
import System
clr.AddReference('ProtoGeometry')
from Autodesk.DesignScript.Geometry import *
dirAppLoc = System.Environment.GetFolderPath(System.Environment.SpecialFolder.LocalApplicationData)
sys.path.append(dirAppLoc + r'\python-3.8.3-embed-amd64\Lib\site-packages')
clr.AddReference('System.Drawing')
import System.Drawing
from System.Drawing import *
from System.Drawing.Imaging import *
from System.IO import MemoryStream
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
import io
import math
import numpy as np
from scipy import spatial
import matplotlib.pyplot as plt
def plt2arr(fig):
"""
need to draw if figure is not drawn yet
"""
fig.canvas.draw()
rgba_buf = fig.canvas.buffer_rgba()
(w,h) = fig.canvas.get_width_height()
rgba_arr = np.frombuffer(rgba_buf, dtype=np.uint8).reshape((h,w,4))
return rgba_arr
def convertToBitmap2(npImgArray):
bitmap_ = None
# remove alpha
if npImgArray.ndim == 3 and npImgArray.shape[-1] == 4:
npImgArray = npImgArray[:, :, :-1]
# convert to PIL Image
if npImgArray.ndim == 3:
image = Image.fromarray(npImgArray, "RGB")
else:
image = Image.fromarray(npImgArray, "L")
# convert to Python ByteArray
byteIO = io.BytesIO()
image.save(byteIO, format='BMP')
byteArr = byteIO.getvalue()
# convert to Net ByteArray
netBytes = System.Array[System.Byte](byteArr)
with MemoryStream(netBytes) as ms:
bitmap_ = Bitmap(ms)
return bitmap_
#num_points = 10
X=IN[0]
num_points=IN[1]
#points_coordinate = np.random.rand(num_points, 2) # generate coordinate of points
#points_coordinate = np.array([[50 , 6],[100, 0.23430645],[0.26308841, 0.25977513],[53 , 600]])
#points_coordinate = np.array([[pt[0],pt[1]] for pt in IN[0]], dtype=np.float32)
points_coordinate = np.array([[pt.X, pt.Y] for pt in IN[0]], dtype=np.float32)
distance_matrix = spatial.distance.cdist(points_coordinate, points_coordinate, metric='euclidean')
def cal_total_distance(routine):
'''The objective function. input routine, return total distance.
cal_total_distance(np.arange(num_points))
'''
num_points, = routine.shape
return sum([distance_matrix[routine[i % num_points], routine[(i + 1) % num_points]] for i in range(num_points)])
from sko.GA import GA_TSP
ga_tsp = GA_TSP(func=cal_total_distance, n_dim=num_points, size_pop=50, max_iter=5000, prob_mut=1)
best_points, best_distance = ga_tsp.run()
points_coordinate
fig, ax = plt.subplots(1, 2)
best_points_ = np.concatenate([best_points, [best_points[0]]])
best_points_
best_points_coordinate = points_coordinate[best_points_, :]
ax[0].plot(best_points_coordinate[:, 0], best_points_coordinate[:, 1], 'o-r')
ax[1].plot(ga_tsp.generation_best_Y)
plt.show()
image_from_plot = plt2arr(fig)
bitmap1 = convertToBitmap2(image_from_plot)
OUT = bitmap1,best_points_coordinate.tolist()
