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Commit 5bffc7ae by Ari Whuandaniel Manurung
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Commitv3

parent d11f1263
Showing with 492 additions and 28 deletions
.ipynb_checkpoints/Swarm ACO-checkpoint.ipynb 0 → 100644
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Analisis Masalah\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Source Code, "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import math\n",
"import random\n",
"import time\n",
"import pandas as pd\n",
"import csv\n",
"import sys\n",
"import datetime\n",
"import timeit"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"start = datetime.datetime.strptime(\"21-07-2020\", \"%d-%m-%Y\")\n",
"end = datetime.datetime.strptime(\"22-07-2020\", \"%d-%m-%Y\")\n",
"date_generated = [start + datetime.timedelta(days=x) for x in range(0, (end-start).days)]\n",
"#print(len(date_generated))"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"cost = 400000\n",
"Cost = int(cost)\n",
"min_cost = 399333"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"Weather = 26.897694"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"class Fitness_value:\n",
" def getting_max_distance():\n",
" max_distance = 0 \n",
" max_distance += len(date_generated) * 720\n",
" return max_distance\n",
" def getting_max_cost():\n",
" max_cost = 0\n",
" max_cost +=Cost\n",
" return max_cost"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"#Load dataset sebelum integrasi\n",
"#Datax = pd.read_csv('./Yolanda/tri/Data Toba Samosir_Sheet3.csv')\n",
"#Datax.drop(Datax.filter(regex=\"Unname\"),axis=1, inplace=True)\n",
"Datax = pd.read_csv('./Data Toba Samosir_Sheet3.csv')\n",
"Datax.drop(Datax.filter(regex=\"Unname\"),axis=1, inplace=True)\n",
"Dataz = pd.read_csv('./List_city.csv')\n",
"Dat = Datax.to_numpy()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0. , 50.9, 12.8, ..., 22.5, 14.4, 5.7],\n",
" [50.9, 0. , 59.2, ..., 32.9, 60.8, 52.1],\n",
" [12.8, 59.2, 0. , ..., 31.8, 4.5, 7.7],\n",
" ...,\n",
" [23.5, 32.9, 31.8, ..., 0. , 33.4, 24.7],\n",
" [14.4, 60.8, 4.5, ..., 33.4, 0. , 9.2],\n",
" [ 5.7, 52.1, 7.7, ..., 24.7, 9.2, 0. ]])"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Dat"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"#id_city = list(Dataz['ID_City'])\n",
"#path = random.sample(range(len(id_city)), 7)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"#Datax = pd.read_csv('./Yolanda/tri/Data Toba Samosir_Sheet1.csv')\n",
"#Datax.drop(Datax.filter(regex=\"Unname\"),axis=1, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"def generate_start_nodes (individu,n_kota):\n",
" \"\"\"\n",
" :param individu:(int) jumlah semut\n",
" :param n_kota :(int) jumlah kota\n",
" :return : tabulist yang masih berisi start (dan end) node untuk semua individu semut\n",
" \"\"\"\n",
" start_nodes= []\n",
" for i in range (individu): \n",
" start_nodes.append([0 for i in range(n_kota+1)])\n",
" start_nodes[-1][0] = random.randint(0,(n_kota-1))\n",
" start_nodes[-1][-1] = start_nodes[-1][0]\n",
" return start_nodes"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"def evaluate_best_city (kota_sekarang, matriks_jarak ,tau, tabulist, alpha, beta):\n",
" \"\"\"\n",
" Mengevaluasi kota terbaik yang memungkinkan berdasarkan probabilitas dan tabulist\n",
" :param kota_sekarang: (int 0..15) \n",
" :param tabulist : (int[0..15]) merupakan kota yang sudah pernah dikunjungi oleh semut\n",
" :param tau : (list) intensitas pheromon pada \n",
" :param matriks_jarak: (matriks[16][16]) matriks yang berisi jarak antar kota\n",
" :return : kota dengan probabilitas tertinggi\n",
" \"\"\"\n",
" probability = [0] * np.size(matriks_jarak[1])\n",
" allowed = []\n",
" allowed[:] = [x for x in range(np.size(matriks_jarak[1])) if x not in tabulist]\n",
" p = 0\n",
" for i in allowed:\n",
" visibility = 1/matriks_jarak[kota_sekarang][i]\n",
" p += (tau[kota_sekarang][i]**alpha)*(visibility**beta)\n",
" for i in allowed:\n",
" visibility = 1/matriks_jarak[kota_sekarang][i]\n",
" probability[i] = (tau[kota_sekarang][i]**alpha)*(visibility**beta)/p\n",
" tmp = max(probability)\n",
" return probability.index(tmp)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"def update_tau(individu, tau, tabulists, L, evaporation_coeff, Q,n_kota):\n",
" \"\"\"\n",
" :param evaporation: koefisien evaporation\n",
" :param tau : matriks tau seukuran kota x kota\n",
" :param L : matriks/array[1x16] yang mengandung informasi jarak tour yang\n",
" dihasilkan oleh satu semut\n",
" :param delta_tau : matriks seukuran kota x kota\n",
" :return : tau yang baru\n",
" \"\"\"\n",
" for i in range (individu):\n",
" for j in range (n_kota):\n",
" delta_tau = Q/L[i]\n",
" tau[tabulists[i][j],tabulists[i][j+1]] = evaporation_coeff * tau[tabulists[i][j],tabulists[i][j+1]] + delta_tau\n",
" return (tau)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"def hitung_jarak_1_rute(single_solution,matriks_jarak):\n",
" \"\"\"\n",
" :param single_solution: Sebuah tabulist dari seekor semut (1 solusi) [0..16]\n",
" :param matriks_jarak : matriks jarak antar node\n",
" :return : Total jarak yang dihasilkan solusi tersebut\n",
" \"\"\"\n",
" jarak = 0\n",
" for i in range (len(single_solution)-1):\n",
" jarak += matriks_jarak[single_solution[i]][single_solution[i+1]] \n",
" return (jarak)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"def compute_shortest_distances(tabulists, matriks_jarak):\n",
" \"\"\"\n",
" :param tabulists: Tabulist sejumlah individu x banyak kota\n",
" :return : tuple (jarak terkecil yang di temukan (float)\n",
" rute dengan jarak terkecil (list),\n",
" list jarak (L)(list)\n",
" rata2 jarak yang ditemukan (float))\n",
" :>>>(min_dist,route,L,rerata)\n",
" \"\"\"\n",
" distances = []\n",
" distances = [0] * len(tabulists)\n",
" for i in range(0,len(tabulists)):\n",
" distances[i] = hitung_jarak_1_rute(tabulists[i],matriks_jarak)\n",
" L = distances\n",
" L = L- 0.97*min(L)\n",
" return (min(distances) , tabulists[distances.index(min(distances))] , L, np.mean(distances))"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"#tabulists"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"Maximum_distance = Fitness_value.getting_max_distance()\n",
"Maximum_cost = Fitness_value.getting_max_cost()\n",
"TARGET_weather = Weather\n",
"CITY_COUNT = len(Datax)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"400000"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Maximum_cost"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"running = 1\n",
"global_distance = []\n",
"\n",
"for run in range (running):\n",
" iterasi = 300\n",
" alpha = 2 #jejak pheromon\n",
" beta = 2#visibility\n",
" individu = 40\n",
" n_kota = 7\n",
" evaporation_coeff = 0.1\n",
" Q = 1\n",
" nodelist = []\n",
"\n",
" matriks_jarak = Dat\n",
" tau = np.ones(np.shape(matriks_jarak))*0.5 #init. intensitas pheromon/jejak pada busur\n",
" delta_tau = np.zeros((n_kota,n_kota))\n",
"\n",
" shortest_distance = math.inf #infinit\n",
"\n",
" shortest_route = []\n",
" average = []\n",
" for i in range (iterasi):\n",
" tabulists = generate_start_nodes(individu,n_kota)\n",
" for j in range (1,n_kota):\n",
" for k in range (individu):\n",
" tabulists[k][j] = evaluate_best_city(tabulists[k][j-1],matriks_jarak,tau, tabulists[k],alpha,beta)\n",
" min_dist,route,L,rerata = compute_shortest_distances(tabulists,matriks_jarak)\n",
" average.append(rerata)\n",
"\n",
" if ((shortest_distance > min_dist) and (shortest_distance > Maximum_distance) and (min_cost <=Maximum_cost) and ((Weather >=18) and (Weather <=28))):\n",
" shortest_distance = min_dist\n",
" shortest_route = route\n",
"\n",
" tau = update_tau(individu, tau, tabulists, L, evaporation_coeff, Q,n_kota)\n",
"\n",
" print (\"Route : \",route)\n",
" print (shortest_distance,rerata,\",\",min_cost,\",\",Weather)\n",
" global_distance.append(shortest_distance)\n",
" print(\"\\n\")\n",
"\n",
"for shortest in global_distance:\n",
" print (shortest) \n",
"\n",
"\n",
"#print (\"Rata-rata rute terpendek yang dihasilkan: \",np.average(global_distance))\n"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[2, 15, 24, 20, 31, 23, 22, 2]"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"route"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"def decoding_ACO():\n",
" name = 0\n",
" for i in range(len(route)):\n",
" name = route[i]\n",
" print(Dataz.iloc[name][1])\n",
" i+=1\n",
" return "
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Pakkodian\n",
"Bukit Tarabunga\n",
"Dolok Tolong\n",
"Lumban Silintong\n",
"Monumen Raja Sonakmalela\n",
"Balerong Onan Balige\n",
"Pantai Janji Maria\n",
"Pakkodian\n"
]
}
],
"source": [
"decoding_ACO()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
Swarm ACO.ipynb
...@@ -298,7 +298,7 @@ ...@@ -298,7 +298,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 18, "execution_count": 30,
"metadata": { "metadata": {
"scrolled": true "scrolled": true
}, },
...@@ -307,24 +307,39 @@ ...@@ -307,24 +307,39 @@
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"Route : [2, 14, 35, 20, 31, 23, 5, 2]\n", "Route : [4, 23, 31, 14, 32, 4]\n",
"17.68 , 399333 , 26.897694\n", "27.43 , 399333 , 26.897694\n",
"\n", "\n",
"\n", "\n",
"17.68\n" "Route : [4, 31, 23, 32, 15, 4]\n",
"45.42999999999999 , 399333 , 26.897694\n",
"\n",
"\n",
"Route : [2, 35, 14, 20, 16, 2]\n",
"25.150000000000002 , 399333 , 26.897694\n",
"\n",
"\n",
"Route : [2, 35, 23, 31, 15, 2]\n",
"22.730000000000004 , 399333 , 26.897694\n",
"\n",
"\n",
"Route : [2, 16, 24, 18, 7, 2]\n",
"59.3 , 399333 , 26.897694\n",
"\n",
"\n"
] ]
} }
], ],
"source": [ "source": [
"running = 1\n", "running = 5\n",
"global_distance = []\n", "global_distance = []\n",
"\n", "\n",
"for run in range (running):\n", "for run in range (running):\n",
" iterasi = 300\n", " iterasi = 50\n",
" alpha = 2 #jejak pheromon\n", " alpha = 2 #jejak pheromon\n",
" beta = 2#visibility\n", " beta = 2#visibility\n",
" individu = 40\n", " individu = 40\n",
" n_kota = 7\n", " n_kota = 5\n",
" evaporation_coeff = 0.1\n", " evaporation_coeff = 0.1\n",
" Q = 1\n", " Q = 1\n",
" nodelist = []\n", " nodelist = []\n",
...@@ -352,12 +367,12 @@ ...@@ -352,12 +367,12 @@
" tau = update_tau(individu, tau, tabulists, L, evaporation_coeff, Q,n_kota)\n", " tau = update_tau(individu, tau, tabulists, L, evaporation_coeff, Q,n_kota)\n",
"\n", "\n",
" print (\"Route : \",route)\n", " print (\"Route : \",route)\n",
" print (shortest_distance,\",\",min_cost,\",\",Weather)\n", " print (min_dist,\",\",min_cost,\",\",Weather)\n",
" global_distance.append(shortest_distance)\n", " global_distance.append(shortest_distance)\n",
" print(\"\\n\")\n", " print(\"\\n\")\n",
"\n", "\n",
"for shortest in global_distance:\n", "#for shortest in global_distance:\n",
" print (shortest) \n", "# print (shortest) \n",
"\n", "\n",
"\n", "\n",
"#print (\"Rata-rata rute terpendek yang dihasilkan: \",np.average(global_distance))\n" "#print (\"Rata-rata rute terpendek yang dihasilkan: \",np.average(global_distance))\n"
...@@ -365,16 +380,16 @@ ...@@ -365,16 +380,16 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 19, "execution_count": 32,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"[2, 14, 35, 20, 31, 23, 5, 2]" "[2, 16, 24, 18, 7, 2]"
] ]
}, },
"execution_count": 19, "execution_count": 32,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -385,7 +400,16 @@ ...@@ -385,7 +400,16 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 20, "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#route = []"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -400,7 +424,7 @@ ...@@ -400,7 +424,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 21, "execution_count": 37,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -408,12 +432,10 @@ ...@@ -408,12 +432,10 @@
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"Pakkodian\n", "Pakkodian\n",
"Museum T. B. Silalahi Center\n", "Pantai Meat\n",
"Makam Raja Sisingamangaraja XII\n", "Dolok Tolong\n",
"Lumban Silintong\n", "Air Terjun Siboruon\n",
"Monumen Raja Sonakmalela\n", "Air Terjun Pandumaan\n",
"Balerong Onan Balige\n",
"Bukit Pahoda\n",
"Pakkodian\n" "Pakkodian\n"
] ]
} }
...@@ -428,13 +450,6 @@ ...@@ -428,13 +450,6 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [] "source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
} }
], ],
"metadata": { "metadata": {
......
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