Skip to content

C
Code_Sidang_Ulang
Commit 20ca7f69 by Yolanda
Options

update code

parent 7b81b789
Showing with 344 additions and 239 deletions
TA14 Sidang.ipynb
......@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
"execution_count": 11,
"execution_count": 1,
"metadata": {},
"outputs": [
{
......@@ -33,7 +33,7 @@
},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
......@@ -57,7 +57,7 @@
},
{
"cell_type": "code",
"execution_count": 96,
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
......@@ -77,7 +77,7 @@
},
{
"cell_type": "code",
"execution_count": 14,
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
......@@ -96,7 +96,7 @@
},
{
"cell_type": "code",
"execution_count": 15,
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
......@@ -120,7 +120,7 @@
},
{
"cell_type": "code",
"execution_count": 16,
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
......@@ -133,7 +133,7 @@
},
{
"cell_type": "code",
"execution_count": 17,
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
......@@ -144,7 +144,7 @@
},
{
"cell_type": "code",
"execution_count": 70,
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
......@@ -161,7 +161,7 @@
},
{
"cell_type": "code",
"execution_count": 71,
"execution_count": 9,
"metadata": {},
"outputs": [
{
......@@ -169,54 +169,54 @@
"output_type": "stream",
"text": [
"Epoch 1/20\n",
"62/62 [==============================] - 3s 55ms/step - loss: 0.2066 - acc: 0.0806\n",
"62/62 [==============================] - 3s 52ms/step - loss: 0.2133 - acc: 0.0484\n",
"Epoch 2/20\n",
"62/62 [==============================] - 0s 4ms/step - loss: 0.1949 - acc: 0.0645\n",
"62/62 [==============================] - 0s 3ms/step - loss: 0.2018 - acc: 0.0645\n",
"Epoch 3/20\n",
"62/62 [==============================] - 0s 3ms/step - loss: 0.1787 - acc: 0.1129\n",
"62/62 [==============================] - 0s 4ms/step - loss: 0.1899 - acc: 0.0645\n",
"Epoch 4/20\n",
"62/62 [==============================] - 0s 3ms/step - loss: 0.1603 - acc: 0.0645\n",
"62/62 [==============================] - 0s 3ms/step - loss: 0.1752 - acc: 0.0161\n",
"Epoch 5/20\n",
"62/62 [==============================] - 0s 3ms/step - loss: 0.1375 - acc: 0.0968\n",
"62/62 [==============================] - 0s 3ms/step - loss: 0.1560 - acc: 0.0323\n",
"Epoch 6/20\n",
"62/62 [==============================] - 0s 3ms/step - loss: 0.1191 - acc: 0.0806\n",
"62/62 [==============================] - 0s 3ms/step - loss: 0.1399 - acc: 0.0323\n",
"Epoch 7/20\n",
"62/62 [==============================] - 0s 4ms/step - loss: 0.1010 - acc: 0.0806\n",
"62/62 [==============================] - 0s 3ms/step - loss: 0.1208 - acc: 0.0323\n",
"Epoch 8/20\n",
"62/62 [==============================] - 0s 4ms/step - loss: 0.0917 - acc: 0.1129\n",
"62/62 [==============================] - 0s 4ms/step - loss: 0.1094 - acc: 0.0161\n",
"Epoch 9/20\n",
"62/62 [==============================] - 0s 4ms/step - loss: 0.0840 - acc: 0.0484\n",
"62/62 [==============================] - 0s 3ms/step - loss: 0.0975 - acc: 0.0161\n",
"Epoch 10/20\n",
"62/62 [==============================] - 0s 5ms/step - loss: 0.0738 - acc: 0.0323\n",
"62/62 [==============================] - 0s 3ms/step - loss: 0.0868 - acc: 0.0323\n",
"Epoch 11/20\n",
"62/62 [==============================] - 0s 4ms/step - loss: 0.0688 - acc: 0.0484\n",
"62/62 [==============================] - 0s 3ms/step - loss: 0.0821 - acc: 0.0806\n",
"Epoch 12/20\n",
"62/62 [==============================] - 0s 4ms/step - loss: 0.0613 - acc: 0.0645\n",
"62/62 [==============================] - 0s 3ms/step - loss: 0.0750 - acc: 0.0323\n",
"Epoch 13/20\n",
"62/62 [==============================] - 0s 4ms/step - loss: 0.0574 - acc: 0.0484\n",
"62/62 [==============================] - 0s 3ms/step - loss: 0.0704 - acc: 0.0645\n",
"Epoch 14/20\n",
"62/62 [==============================] - 0s 3ms/step - loss: 0.0530 - acc: 0.0806\n",
"62/62 [==============================] - 0s 3ms/step - loss: 0.0614 - acc: 0.0645\n",
"Epoch 15/20\n",
"62/62 [==============================] - 0s 4ms/step - loss: 0.0514 - acc: 0.0645\n",
"62/62 [==============================] - 0s 4ms/step - loss: 0.0612 - acc: 0.0000e+00\n",
"Epoch 16/20\n",
"62/62 [==============================] - 0s 5ms/step - loss: 0.0494 - acc: 0.0161\n",
"62/62 [==============================] - 0s 3ms/step - loss: 0.0612 - acc: 0.0000e+00\n",
"Epoch 17/20\n",
"62/62 [==============================] - 0s 3ms/step - loss: 0.0448 - acc: 0.0968\n",
"62/62 [==============================] - 0s 4ms/step - loss: 0.0536 - acc: 0.0000e+00\n",
"Epoch 18/20\n",
"62/62 [==============================] - 0s 4ms/step - loss: 0.0437 - acc: 0.0645\n",
"62/62 [==============================] - 0s 3ms/step - loss: 0.0534 - acc: 0.0323\n",
"Epoch 19/20\n",
"62/62 [==============================] - 0s 4ms/step - loss: 0.0432 - acc: 0.0645\n",
"62/62 [==============================] - 0s 3ms/step - loss: 0.0501 - acc: 0.0000e+00\n",
"Epoch 20/20\n",
"62/62 [==============================] - 0s 3ms/step - loss: 0.0417 - acc: 0.0161\n"
"62/62 [==============================] - 0s 3ms/step - loss: 0.0447 - acc: 0.0161\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.callbacks.History at 0x25c695acac8>"
"<keras.callbacks.callbacks.History at 0x2a29ff19fd0>"
]
},
"execution_count": 71,
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
......@@ -238,7 +238,7 @@
},
{
"cell_type": "code",
"execution_count": 72,
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
......@@ -255,7 +255,7 @@
},
{
"cell_type": "code",
"execution_count": 92,
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
......@@ -266,45 +266,45 @@
},
{
"cell_type": "code",
"execution_count": 93,
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[27.967451],\n",
" [27.607952],\n",
" [28.059902],\n",
" [27.81603 ],\n",
" [27.799353],\n",
" [27.736164],\n",
" [27.613163],\n",
" [26.997648],\n",
" [27.680696],\n",
" [27.724241],\n",
" [26.857187],\n",
" [27.729334],\n",
" [27.896103],\n",
" [27.735334],\n",
" [27.649883],\n",
" [27.663065],\n",
" [27.571318],\n",
" [27.641148],\n",
" [27.764853],\n",
" [27.439972],\n",
" [27.94146 ],\n",
" [27.124084],\n",
" [27.573236],\n",
" [27.68184 ],\n",
" [27.275497],\n",
" [27.024868],\n",
" [27.208172],\n",
" [27.361816],\n",
" [27.742584],\n",
" [27.756859]], dtype=float32)"
"array([[26.699457],\n",
" [27.7065 ],\n",
" [27.927803],\n",
" [27.236782],\n",
" [27.464771],\n",
" [27.885899],\n",
" [27.478645],\n",
" [27.846294],\n",
" [26.694408],\n",
" [27.603418],\n",
" [27.53826 ],\n",
" [27.409962],\n",
" [27.388931],\n",
" [27.556946],\n",
" [27.900398],\n",
" [27.203278],\n",
" [27.552303],\n",
" [26.711037],\n",
" [27.826591],\n",
" [27.691978],\n",
" [27.053234],\n",
" [27.037136],\n",
" [27.26916 ],\n",
" [27.663702],\n",
" [26.765814],\n",
" [27.693802],\n",
" [27.176167],\n",
" [26.76131 ],\n",
" [27.435785],\n",
" [27.47954 ]], dtype=float32)"
]
},
"execution_count": 93,
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
......@@ -315,7 +315,7 @@
},
{
"cell_type": "code",
"execution_count": 97,
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
......@@ -327,16 +327,16 @@
},
{
"cell_type": "code",
"execution_count": 98,
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"27.756859"
"27.47954"
]
},
"execution_count": 98,
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
......@@ -348,14 +348,14 @@
},
{
"cell_type": "code",
"execution_count": 24,
"execution_count": 15,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": "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\n",
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
......@@ -385,7 +385,7 @@
},
{
"cell_type": "code",
"execution_count": 25,
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
......@@ -395,7 +395,7 @@
"#Acceleration Constant\n",
"Acceleration_constant = 2; # Maximum velocity change allowed. Range: 0 >= V_MAX < CITY_COUNT\n",
"#Iterasi\n",
"MAX_EPOCHS = 2\n",
"MAX_EPOCHS = 500\n",
"map = [];\n",
"particles = []\n",
"Maximum_distance= Fitness_value.getting_max_distance()\n",
......@@ -405,7 +405,7 @@
},
{
"cell_type": "code",
"execution_count": 26,
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
......@@ -435,7 +435,7 @@
},
{
"cell_type": "code",
"execution_count": 27,
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
......@@ -475,7 +475,7 @@
},
{
"cell_type": "code",
"execution_count": 28,
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
......@@ -489,7 +489,7 @@
},
{
"cell_type": "code",
"execution_count": 29,
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
......@@ -511,7 +511,7 @@
},
{
"cell_type": "code",
"execution_count": 30,
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
......@@ -530,7 +530,7 @@
},
{
"cell_type": "code",
"execution_count": 31,
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
......@@ -545,7 +545,7 @@
},
{
"cell_type": "code",
"execution_count": 32,
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
......@@ -579,7 +579,7 @@
},
{
"cell_type": "code",
"execution_count": 33,
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
......@@ -611,7 +611,7 @@
},
{
"cell_type": "code",
"execution_count": 34,
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
......@@ -650,7 +650,7 @@
},
{
"cell_type": "code",
"execution_count": 35,
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
......@@ -676,7 +676,7 @@
},
{
"cell_type": "code",
"execution_count": 36,
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
......@@ -694,7 +694,8 @@
" sys.stdout.write(str(particles[i].get_data(j)) + \", \")\n",
" get_total_distance(i)\n",
" get_total_cost(i) \n",
" sys.stdout.write(\"Distance: \" + str(particles[i].get_pBest_distance()) + \"\\n\") \n",
" \n",
" sys.stdout.write(\"Distance: \" + str(particles[i].get_pBest_distance()) + \" , \" \"Cost : \" + str(particles[i].get_pBest_cost()) + \" , \" \"Weather : \" + str(Weather) +\"\\n\")\n",
" if (particles[i].get_pBest_distance() <= Maximum_distance) and (particles[i].get_pBest_cost() <= Maximum_cost) and (Weather>=20 and Weather<=28):\n",
" if (Weather >= 20) and (Weather <=28):\n",
" done = True \n",
......@@ -712,7 +713,7 @@
},
{
"cell_type": "code",
"execution_count": 37,
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
......@@ -733,25 +734,25 @@
},
{
"cell_type": "code",
"execution_count": 38,
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Route: 1, 6, 0, 5, 3, 2, 4, Distance: 305.3\n",
"Route: 1, 6, 5, 0, 2, 3, 4, Distance: 236.39999999999998\n",
"Route: 6, 4, 5, 1, 2, 0, 3, Distance: 251.50000000000003\n",
"Route: 3, 6, 0, 5, 2, 1, 4, Distance: 306.20000000000005\n",
"Route: 5, 2, 4, 3, 6, 0, 1, Distance: 247.3\n",
"Route: 5, 2, 3, 1, 4, 6, 0, Distance: 306.2 , Cost : 45000 , Weather : 27.47954\n",
"Route: 0, 2, 4, 6, 3, 5, 1, Distance: 251.50000000000003 , Cost : 45000 , Weather : 27.47954\n",
"Route: 0, 1, 3, 5, 4, 2, 6, Distance: 256.40000000000003 , Cost : 45000 , Weather : 27.47954\n",
"Route: 5, 0, 4, 2, 6, 3, 1, Distance: 171.70000000000002 , Cost : 45000 , Weather : 27.47954\n",
"Route: 1, 6, 5, 4, 3, 2, 0, Distance: 241.3 , Cost : 45000 , Weather : 27.47954\n",
"Changes for particle 1: 1\n",
"Changes for particle 2: 1\n",
"Changes for particle 3: 1\n",
"Changes for particle 4: 1\n",
"epoch number: 0\n",
"Target reached.\n",
"Best Route: 1, 5, 2, 4, 0, 3, 6, Distance: 157.3 , Cost : 45000 , Weather : 27.510067\n"
"Best Route: 6, 3, 5, 2, 0, 4, 1, Distance: 152.2 , Cost : 45000 , Weather : 27.47954\n"
]
}
],
......@@ -764,16 +765,16 @@
},
{
"cell_type": "code",
"execution_count": 39,
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[1, 5, 2, 4, 0, 3, 6]"
"[6, 3, 5, 2, 0, 4, 1]"
]
},
"execution_count": 39,
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
......@@ -792,7 +793,7 @@
},
{
"cell_type": "code",
"execution_count": 40,
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
......@@ -808,20 +809,20 @@
},
{
"cell_type": "code",
"execution_count": 41,
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"BUKIT travel Gibeon\n",
"Bukit Senyum\n",
"Taman Eden 100 Tobasa\n",
"Bukit Pahoda\n",
"Pakkodian\n",
"Water Park Tambunan\n",
"Pantai BUL BUL\n",
"Taman Eden 100 Tobasa\n",
"Bukit Senyum\n"
"Water Park Tambunan\n",
"BUKIT travel Gibeon\n"
]
}
],
......@@ -838,7 +839,7 @@
},
{
"cell_type": "code",
"execution_count": 42,
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
......@@ -855,7 +856,7 @@
},
{
"cell_type": "code",
"execution_count": 43,
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
......@@ -878,7 +879,7 @@
},
{
"cell_type": "code",
"execution_count": 44,
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
......@@ -900,7 +901,7 @@
},
{
"cell_type": "code",
"execution_count": 45,
"execution_count": 38,
"metadata": {},
"outputs": [],
"source": [
......@@ -918,7 +919,7 @@
},
{
"cell_type": "code",
"execution_count": 46,
"execution_count": 39,
"metadata": {},
"outputs": [],
"source": [
......@@ -942,7 +943,7 @@
},
{
"cell_type": "code",
"execution_count": 47,
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
......@@ -960,7 +961,7 @@
},
{
"cell_type": "code",
"execution_count": 48,
"execution_count": 41,
"metadata": {},
"outputs": [],
"source": [
......@@ -978,7 +979,7 @@
},
{
"cell_type": "code",
"execution_count": 49,
"execution_count": 42,
"metadata": {},
"outputs": [],
"source": [
......@@ -990,31 +991,31 @@
},
{
"cell_type": "code",
"execution_count": 50,
"execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Route : [0, 22, 5, 4, 34, 16, 21, 0]\n",
"118.8 , 20000 , 27.510067\n",
"Route : [5, 35, 14, 23, 31, 29, 18, 5]\n",
"92.07999999999998 , 10000 , 27.47954\n",
"\n",
"\n",
"Route : [4, 31, 23, 22, 12, 19, 29, 4]\n",
"189.13 , 30000 , 27.510067\n",
"Route : [0, 23, 31, 20, 27, 5, 24, 0]\n",
"94.83 , 10000 , 27.47954\n",
"\n",
"\n",
"Route : [6, 8, 24, 35, 14, 2, 15, 6]\n",
"158.35 , 25000 , 27.510067\n",
"Route : [0, 35, 14, 4, 31, 23, 7, 0]\n",
"41.98 , 25000 , 27.47954\n",
"\n",
"\n",
"Route : [0, 35, 14, 20, 23, 31, 4, 0]\n",
"22.08 , 35000 , 27.510067\n",
"Route : [0, 35, 14, 19, 3, 1, 13, 0]\n",
"122.55 , 35000 , 27.47954\n",
"\n",
"\n",
"Route : [5, 23, 31, 33, 15, 35, 14, 5]\n",
"67.18 , 12000 , 27.510067\n",
"Route : [0, 23, 31, 18, 33, 14, 35, 0]\n",
"68.68 , 12000 , 27.47954\n",
"\n",
"\n"
]
......@@ -1070,7 +1071,7 @@
},
{
"cell_type": "code",
"execution_count": 51,
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
......@@ -1085,21 +1086,21 @@
},
{
"cell_type": "code",
"execution_count": 52,
"execution_count": 45,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Bukit Pahoda\n",
"Pantai BUL BUL\n",
"Balerong Onan Balige\n",
"Monumen Raja Sonakmalela\n",
"Air Terjun Siboruon\n",
"Pantai Pasifik Porsea\n",
"Bukit Tarabunga\n",
"Makam Raja Sisingamangaraja XII\n",
"Museum T. B. Silalahi Center\n",
"Bukit Pahoda\n"
"Makam Raja Sisingamangaraja XII\n",
"Pantai BUL BUL\n"
]
}
],
......@@ -1116,7 +1117,7 @@
},
{
"cell_type": "code",
"execution_count": 53,
"execution_count": 46,
"metadata": {},
"outputs": [],
"source": [
......@@ -1132,7 +1133,7 @@
},
{
"cell_type": "code",
"execution_count": 54,
"execution_count": 47,
"metadata": {},
"outputs": [],
"source": [
......@@ -1142,7 +1143,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 48,
"metadata": {},
"outputs": [],
"source": [
......@@ -1151,7 +1152,7 @@
},
{
"cell_type": "code",
"execution_count": 55,
"execution_count": 49,
"metadata": {},
"outputs": [],
"source": [
......@@ -1175,7 +1176,7 @@
},
{
"cell_type": "code",
"execution_count": 56,
"execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
......@@ -1193,7 +1194,7 @@
},
{
"cell_type": "code",
"execution_count": 57,
"execution_count": 51,
"metadata": {},
"outputs": [],
"source": [
......@@ -1205,7 +1206,7 @@
},
{
"cell_type": "code",
"execution_count": 58,
"execution_count": 52,
"metadata": {},
"outputs": [],
"source": [
......@@ -1228,7 +1229,7 @@
},
{
"cell_type": "code",
"execution_count": 59,
"execution_count": 53,
"metadata": {},
"outputs": [],
"source": [
......@@ -1241,7 +1242,7 @@
},
{
"cell_type": "code",
"execution_count": 60,
"execution_count": 54,
"metadata": {},
"outputs": [],
"source": [
......@@ -1261,7 +1262,7 @@
},
{
"cell_type": "code",
"execution_count": 61,
"execution_count": 55,
"metadata": {},
"outputs": [],
"source": [
......@@ -1277,7 +1278,7 @@
},
{
"cell_type": "code",
"execution_count": 62,
"execution_count": 56,
"metadata": {},
"outputs": [],
"source": [
......@@ -1304,7 +1305,7 @@
},
{
"cell_type": "code",
"execution_count": 63,
"execution_count": 57,
"metadata": {},
"outputs": [],
"source": [
......@@ -1321,7 +1322,7 @@
},
{
"cell_type": "code",
"execution_count": 64,
"execution_count": 58,
"metadata": {},
"outputs": [],
"source": [
......@@ -1340,17 +1341,17 @@
},
{
"cell_type": "code",
"execution_count": 65,
"execution_count": 59,
"metadata": {},
"outputs": [],
"source": [
"def main():\n",
" # Control parameters\n",
" population =40\n",
" population = 40\n",
" forager_percent = 0.5\n",
" onlooker_percent = 0.4\n",
" role_percent = [onlooker_percent, forager_percent]\n",
" scout_percent = 0.01\n",
" scout_percent = 0.1\n",
" scout_count = math.ceil(population * scout_percent)\n",
" forager_limit = 500\n",
" cycle_limit = 100\n",
......@@ -1391,7 +1392,7 @@
},
{
"cell_type": "code",
"execution_count": 66,
"execution_count": 60,
"metadata": {
"scrolled": false
},
......@@ -1401,154 +1402,258 @@
"output_type": "stream",
"text": [
"CYCLE: 1\n",
"PATH: [16, 20, 7, 13, 21, 14, 35]\n",
"DISTANCE: 180.95000000000002\n",
"COST: 25000\n",
"TEMPERATURE: 27.510066986083984\n",
"PATH: [14, 18, 29, 1, 13, 9, 15]\n",
"DISTANCE: 209.5\n",
"COST: 20000\n",
"TEMPERATURE: 27.47953987121582\n",
"BEE: F\n",
"\n",
"\n",
"CYCLE: 6\n",
"PATH: [35, 14, 20, 16, 7, 13, 21]\n",
"DISTANCE: 180.95\n",
"COST: 25000\n",
"TEMPERATURE: 27.510066986083984\n",
"CYCLE: 4\n",
"PATH: [15, 29, 9, 13, 1, 18, 14]\n",
"DISTANCE: 209.49999999999997\n",
"COST: 20000\n",
"TEMPERATURE: 27.47953987121582\n",
"BEE: R\n",
"\n",
"\n",
"CYCLE: 15\n",
"PATH: [1, 9, 29, 15, 14, 18, 13]\n",
"DISTANCE: 209.49999999999994\n",
"COST: 20000\n",
"TEMPERATURE: 27.47953987121582\n",
"BEE: F\n",
"\n",
"\n",
"CYCLE: 80\n",
"PATH: [13, 1, 29, 18, 15, 14, 9]\n",
"DISTANCE: 209.4\n",
"COST: 20000\n",
"TEMPERATURE: 27.47953987121582\n",
"BEE: R\n",
"\n",
"\n",
"CYCLE: 1\n",
"PATH: [20, 16, 14, 35, 13, 21, 7]\n",
"DISTANCE: 181.04999999999998\n",
"COST: 25000\n",
"TEMPERATURE: 27.510066986083984\n",
"PATH: [9, 29, 15, 14, 18, 13, 1]\n",
"DISTANCE: 209.49999999999997\n",
"COST: 20000\n",
"TEMPERATURE: 27.47953987121582\n",
"BEE: F\n",
"\n",
"\n",
"CYCLE: 2\n",
"PATH: [16, 13, 21, 7, 20, 14, 35]\n",
"DISTANCE: 180.95\n",
"COST: 25000\n",
"TEMPERATURE: 27.510066986083984\n",
"CYCLE: 23\n",
"PATH: [18, 15, 14, 9, 13, 1, 29]\n",
"DISTANCE: 209.39999999999998\n",
"COST: 20000\n",
"TEMPERATURE: 27.47953987121582\n",
"BEE: F\n",
"\n",
"\n",
"CYCLE: 1\n",
"PATH: [35, 14, 20, 16, 7, 21, 13]\n",
"DISTANCE: 180.95\n",
"COST: 25000\n",
"TEMPERATURE: 27.510066986083984\n",
"PATH: [18, 29, 9, 13, 1, 14, 15]\n",
"DISTANCE: 209.5\n",
"COST: 20000\n",
"TEMPERATURE: 27.47953987121582\n",
"BEE: F\n",
"\n",
"\n",
"CYCLE: 1\n",
"PATH: [7, 20, 35, 14, 16, 13, 21]\n",
"DISTANCE: 181.35\n",
"COST: 25000\n",
"TEMPERATURE: 27.510066986083984\n",
"PATH: [9, 29, 15, 14, 18, 13, 1]\n",
"DISTANCE: 209.49999999999997\n",
"COST: 20000\n",
"TEMPERATURE: 27.47953987121582\n",
"BEE: R\n",
"\n",
"\n",
"CYCLE: 17\n",
"PATH: [14, 9, 13, 1, 29, 18, 15]\n",
"DISTANCE: 209.4\n",
"COST: 20000\n",
"TEMPERATURE: 27.47953987121582\n",
"BEE: F\n",
"\n",
"\n",
"CYCLE: 2\n",
"PATH: [35, 16, 20, 21, 13, 7, 14]\n",
"DISTANCE: 181.05\n",
"COST: 25000\n",
"TEMPERATURE: 27.510066986083984\n",
"CYCLE: 53\n",
"PATH: [18, 15, 14, 9, 13, 1, 29]\n",
"DISTANCE: 209.39999999999998\n",
"COST: 20000\n",
"TEMPERATURE: 27.47953987121582\n",
"BEE: F\n",
"\n",
"\n",
"CYCLE: 3\n",
"PATH: [21, 20, 16, 14, 35, 7, 13]\n",
"DISTANCE: 181.04999999999998\n",
"COST: 25000\n",
"TEMPERATURE: 27.510066986083984\n",
"BEE: R\n",
"CYCLE: 1\n",
"PATH: [29, 1, 13, 9, 14, 18, 15]\n",
"DISTANCE: 213.7\n",
"COST: 20000\n",
"TEMPERATURE: 27.47953987121582\n",
"BEE: F\n",
"\n",
"\n",
"CYCLE: 5\n",
"PATH: [21, 35, 14, 20, 16, 7, 13]\n",
"DISTANCE: 180.95\n",
"COST: 25000\n",
"TEMPERATURE: 27.510066986083984\n",
"CYCLE: 2\n",
"PATH: [1, 9, 29, 15, 14, 18, 13]\n",
"DISTANCE: 209.49999999999994\n",
"COST: 20000\n",
"TEMPERATURE: 27.47953987121582\n",
"BEE: F\n",
"\n",
"\n",
"CYCLE: 32\n",
"PATH: [18, 15, 14, 9, 13, 1, 29]\n",
"DISTANCE: 209.39999999999998\n",
"COST: 20000\n",
"TEMPERATURE: 27.47953987121582\n",
"BEE: R\n",
"\n",
"\n",
"CYCLE: 1\n",
"PATH: [16, 7, 13, 21, 20, 35, 14]\n",
"DISTANCE: 181.35\n",
"COST: 25000\n",
"TEMPERATURE: 27.510066986083984\n",
"PATH: [14, 15, 9, 13, 1, 18, 29]\n",
"DISTANCE: 230.09999999999997\n",
"COST: 20000\n",
"TEMPERATURE: 27.47953987121582\n",
"BEE: F\n",
"\n",
"\n",
"CYCLE: 1\n",
"PATH: [35, 14, 16, 20, 13, 21, 7]\n",
"DISTANCE: 180.95\n",
"COST: 25000\n",
"TEMPERATURE: 27.510066986083984\n",
"PATH: [9, 18, 14, 15, 29, 1, 13]\n",
"DISTANCE: 209.5\n",
"COST: 20000\n",
"TEMPERATURE: 27.47953987121582\n",
"BEE: R\n",
"\n",
"\n",
"CYCLE: 7\n",
"PATH: [18, 15, 14, 9, 13, 1, 29]\n",
"DISTANCE: 209.39999999999998\n",
"COST: 20000\n",
"TEMPERATURE: 27.47953987121582\n",
"BEE: R\n",
"\n",
"\n",
"CYCLE: 1\n",
"PATH: [7, 16, 20, 14, 35, 13, 21]\n",
"DISTANCE: 181.35\n",
"COST: 25000\n",
"TEMPERATURE: 27.510066986083984\n",
"PATH: [14, 18, 9, 13, 1, 29, 15]\n",
"DISTANCE: 209.5\n",
"COST: 20000\n",
"TEMPERATURE: 27.47953987121582\n",
"BEE: F\n",
"\n",
"\n",
"CYCLE: 1\n",
"PATH: [21, 13, 14, 35, 16, 20, 7]\n",
"DISTANCE: 180.95\n",
"COST: 25000\n",
"TEMPERATURE: 27.510066986083984\n",
"CYCLE: 2\n",
"PATH: [14, 15, 9, 13, 1, 29, 18]\n",
"DISTANCE: 209.49999999999997\n",
"COST: 20000\n",
"TEMPERATURE: 27.47953987121582\n",
"BEE: F\n",
"\n",
"\n",
"CYCLE: 2\n",
"PATH: [15, 14, 9, 13, 1, 29, 18]\n",
"DISTANCE: 209.4\n",
"COST: 20000\n",
"TEMPERATURE: 27.47953987121582\n",
"BEE: R\n",
"\n",
"\n",
"CYCLE: 16\n",
"PATH: [18, 15, 14, 9, 13, 1, 29]\n",
"DISTANCE: 209.39999999999998\n",
"COST: 20000\n",
"TEMPERATURE: 27.47953987121582\n",
"BEE: R\n",
"\n",
"\n",
"CYCLE: 1\n",
"PATH: [7, 21, 13, 14, 35, 16, 20]\n",
"DISTANCE: 180.95\n",
"COST: 25000\n",
"TEMPERATURE: 27.510066986083984\n",
"PATH: [18, 14, 29, 9, 13, 1, 15]\n",
"DISTANCE: 213.9\n",
"COST: 20000\n",
"TEMPERATURE: 27.47953987121582\n",
"BEE: F\n",
"\n",
"\n",
"CYCLE: 1\n",
"PATH: [21, 35, 14, 20, 16, 7, 13]\n",
"DISTANCE: 180.95\n",
"COST: 25000\n",
"TEMPERATURE: 27.510066986083984\n",
"PATH: [29, 18, 14, 15, 9, 13, 1]\n",
"DISTANCE: 209.5\n",
"COST: 20000\n",
"TEMPERATURE: 27.47953987121582\n",
"BEE: R\n",
"\n",
"\n",
"CYCLE: 2\n",
"PATH: [13, 1, 29, 18, 14, 15, 9]\n",
"DISTANCE: 209.49999999999997\n",
"COST: 20000\n",
"TEMPERATURE: 27.47953987121582\n",
"BEE: F\n",
"\n",
"\n",
"CYCLE: 1\n",
"PATH: [20, 16, 14, 35, 13, 21, 7]\n",
"DISTANCE: 181.04999999999998\n",
"COST: 25000\n",
"TEMPERATURE: 27.510066986083984\n",
"CYCLE: 9\n",
"PATH: [29, 18, 15, 14, 9, 13, 1]\n",
"DISTANCE: 209.4\n",
"COST: 20000\n",
"TEMPERATURE: 27.47953987121582\n",
"BEE: F\n",
"\n",
"\n",
"CYCLE: 1\n",
"PATH: [20, 16, 7, 13, 21, 35, 14]\n",
"DISTANCE: 180.95\n",
"COST: 25000\n",
"TEMPERATURE: 27.510066986083984\n",
"PATH: [1, 29, 18, 14, 15, 9, 13]\n",
"DISTANCE: 209.49999999999997\n",
"COST: 20000\n",
"TEMPERATURE: 27.47953987121582\n",
"BEE: F\n",
"\n",
"\n",
"CYCLE: 8\n",
"PATH: [14, 9, 13, 1, 29, 18, 15]\n",
"DISTANCE: 209.4\n",
"COST: 20000\n",
"TEMPERATURE: 27.47953987121582\n",
"BEE: R\n",
"\n",
"\n",
"CYCLE: 1\n",
"PATH: [35, 20, 16, 7, 13, 21, 14]\n",
"DISTANCE: 181.35\n",
"COST: 25000\n",
"TEMPERATURE: 27.510066986083984\n",
"PATH: [29, 9, 13, 1, 14, 15, 18]\n",
"DISTANCE: 209.5\n",
"COST: 20000\n",
"TEMPERATURE: 27.47953987121582\n",
"BEE: F\n",
"\n",
"\n",
"CYCLE: 3\n",
"PATH: [16, 7, 13, 21, 35, 14, 20]\n",
"DISTANCE: 180.95\n",
"COST: 25000\n",
"TEMPERATURE: 27.510066986083984\n",
"CYCLE: 2\n",
"PATH: [14, 9, 13, 1, 29, 18, 15]\n",
"DISTANCE: 209.4\n",
"COST: 20000\n",
"TEMPERATURE: 27.47953987121582\n",
"BEE: F\n",
"\n",
"\n",
"CYCLE: 6\n",
"PATH: [18, 15, 14, 9, 13, 1, 29]\n",
"DISTANCE: 209.39999999999998\n",
"COST: 20000\n",
"TEMPERATURE: 27.47953987121582\n",
"BEE: F\n",
"\n",
"\n",
"CYCLE: 1\n",
"PATH: [15, 14, 18, 1, 13, 9, 29]\n",
"DISTANCE: 209.5\n",
"COST: 20000\n",
"TEMPERATURE: 27.47953987121582\n",
"BEE: F\n",
"\n",
"\n",
"CYCLE: 4\n",
"PATH: [18, 1, 13, 9, 29, 15, 14]\n",
"DISTANCE: 209.49999999999997\n",
"COST: 20000\n",
"TEMPERATURE: 27.47953987121582\n",
"BEE: R\n",
"\n",
"\n",
"CYCLE: 65\n",
"PATH: [1, 29, 18, 15, 14, 9, 13]\n",
"DISTANCE: 209.4\n",
"COST: 20000\n",
"TEMPERATURE: 27.47953987121582\n",
"BEE: F\n",
"\n",
"\n"
......@@ -1566,16 +1671,16 @@
},
{
"cell_type": "code",
"execution_count": 67,
"execution_count": 63,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[14, 21, 13, 7, 20, 16, 35]"
"[15, 18, 1, 13, 9, 29, 14]"
]
},
"execution_count": 67,
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
......@@ -1615,20 +1720,20 @@
},
{
"cell_type": "code",
"execution_count": 68,
"execution_count": 64,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Museum T. B. Silalahi Center\n",
"Siregar Aek Nalas\n",
"Bukit Tarabunga\n",
"Air Terjun Siboruon\n",
"BUKIT travel Gibeon\n",
"The Kaldera\n",
"Air Terjun Pandumaan\n",
"Lumban Silintong\n",
"Pantai Meat\n",
"Makam Raja Sisingamangaraja XII\n"
"PANTAI AGADON\n",
"Tornagodang\n",
"Museum T. B. Silalahi Center\n"
]
}
],
......
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment