ant-colony.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "legitimate-greenhouse",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-12-01T14:16:21.833006Z",
     "start_time": "2021-12-01T14:16:21.129969Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import math\n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "from copy import deepcopy\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "architectural-minnesota",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-12-01T14:16:21.910233Z",
     "start_time": "2021-12-01T14:16:21.866916Z"
    }
   },
   "outputs": [],
   "source": [
    "def read_data(test):\n",
    "    path = \"job\"+str(test)+\".assign\"\n",
    "    f = open(path, \"r\")\n",
    "    txt = f.readlines()\n",
    "    mtrx = []\n",
    "    matrix = []\n",
    "    for i,line in enumerate(txt):\n",
    "        mtrx.append(line.split(' '))\n",
    "        matrix.append([])\n",
    "        for j,num in enumerate(mtrx[i]):\n",
    "            if num != '':\n",
    "                matrix[i].append(num)\n",
    "    if test == 1:\n",
    "        for i in range(len(matrix)):\n",
    "            for j in range(len(matrix[i])):\n",
    "                matrix[i][j] = int(matrix[i][j])\n",
    "    \n",
    "    elif test == 2 or test == 3:\n",
    "        for i in range(len(matrix)):\n",
    "            matrix[i].pop(-1)\n",
    "            for j in range(len(matrix[i])):\n",
    "                matrix[i][j] = int(matrix[i][j])\n",
    "    return matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "golden-ultimate",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-12-01T14:16:21.957398Z",
     "start_time": "2021-12-01T14:16:21.944648Z"
    }
   },
   "outputs": [],
   "source": [
    "class Ant:\n",
    "    def __init__(self):\n",
    "        self.path = []\n",
    "        self.cost = 0\n",
    "    \n",
    "    def reset(self):\n",
    "        self.path = []\n",
    "        self.cost = 0\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "defined-abortion",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-12-01T14:40:27.678717Z",
     "start_time": "2021-12-01T14:40:27.647785Z"
    }
   },
   "outputs": [],
   "source": [
    "class Colony:\n",
    "    def __init__(self, ant_num, matrix , base_formun, T_SA, SA_rate, beta, p_local, alpha, q_0):\n",
    "        self.n = len(matrix)\n",
    "        self.matrix = matrix\n",
    "        self.ant_num = ant_num\n",
    "        self.T = T_SA\n",
    "        self.beta = beta\n",
    "        self.alpha = alpha\n",
    "        self.p = p_local\n",
    "        self.base_formun = base_formun\n",
    "        self.q_0 = q_0\n",
    "        self.SA_rate = SA_rate\n",
    "\n",
    "        self.ants = []\n",
    "        for i in range(ant_num):\n",
    "            ant = Ant()\n",
    "            self.ants.append(ant)\n",
    "        \n",
    "            \n",
    "        \n",
    "        self.formun = [[0 for j in range(self.n)] for i in range(self.n)]\n",
    "        for i in range(self.n):\n",
    "            for j in range(self.n):\n",
    "                self.formun[i][j] = self.base_formun / self.matrix[i][j]\n",
    "        self.init_formun = self.formun.copy()\n",
    "    \n",
    "    \n",
    "    def training(self,itteration=100,print_itter = False):\n",
    "        his = []\n",
    "        his_path = []\n",
    "        for itter in range(itteration):\n",
    "#             print(itter,end=': ')\n",
    "            self.spread_ants()\n",
    "            self.formun = self.init_formun\n",
    "            self.T *= self.SA_rate\n",
    "            \n",
    "            for step in range(self.n - 1):\n",
    "                for ant in self.ants:\n",
    "                    self.move(ant)\n",
    "#                     self.local_update_formun(ant)\n",
    "            path, cost = self.find_best_path()\n",
    "            if print_itter == True:\n",
    "                print(cost)\n",
    "            temp_path = deepcopy(path)\n",
    "            temp_path = self.mutation(temp_path)\n",
    "            \n",
    "            delta = self.fitness(temp_path) - self.fitness(path)\n",
    "            if(delta < 0 or random.uniform(0, 1) < math.exp(-delta / self.T)):\n",
    "                path = temp_path\n",
    "                cost = self.fitness(temp_path)\n",
    "            self.global_update_formun(path,cost)\n",
    "            \n",
    "            \n",
    "            his.append(cost)\n",
    "            his_path.append(path)\n",
    "            for ant in self.ants:\n",
    "                ant.reset()\n",
    "        return his,his_path\n",
    "    \n",
    "    \n",
    "    def spread_ants(self):\n",
    "        nodes = list(range(self.n))\n",
    "        random.shuffle(nodes)\n",
    "        for i,ant in enumerate(self.ants):\n",
    "            if i < self.n:\n",
    "                ant.path.append(nodes[i])\n",
    "                ant.cost += self.matrix[0][nodes[i]]\n",
    "            else:\n",
    "                state = random.choice(list(range(self.n)))\n",
    "                ant.path.append(state)\n",
    "                ant.cost += self.matrix[0][state]\n",
    "\n",
    "\n",
    "\n",
    "    def local_update_formun(self,ant):\n",
    "        i = len(ant.path)-1\n",
    "        j = ant.path[-1]\n",
    "        \n",
    "#         delta = 0\n",
    "#         for z in range(self.n):\n",
    "#             if z != j and z != i:\n",
    "#                 temp = 0.7 * self.formun[j][z]\n",
    "#                 if temp > delta:\n",
    "#                     delta = temp\n",
    "        \n",
    "        delta = self.init_formun[i][j]\n",
    "\n",
    "        self.formun[i][j] = ((1-self.p)* self.formun[i][j]) + (self.p*(1/(delta)))\n",
    "\n",
    "    def find_best_path(self):\n",
    "        optima_path = []\n",
    "        best_cost = float('inf')\n",
    "        \n",
    "        for ant in self.ants:\n",
    "            if ant.cost < best_cost:\n",
    "                best_cost = ant.cost\n",
    "                optima_path = ant.path\n",
    "\n",
    "#         print(best_cost)\n",
    "#         print(optima_path)\n",
    "        return optima_path, best_cost\n",
    "\n",
    "    def global_update_formun(self,path,cost):\n",
    "        for i in range(self.n):\n",
    "            self.formun[i][path[i]] += (self.alpha*(cost**-1))\n",
    "                \n",
    "        for i in range(self.n):\n",
    "            for j in range(self.n):\n",
    "                self.formun[i][j] = (1-self.alpha)*self.formun[i][j] \n",
    "    \n",
    "    def select_next_node(self,ant):\n",
    "        q = random.random()\n",
    "        if q < self.q_0:\n",
    "            maxx = 0\n",
    "            selected = None\n",
    "            for node in range(self.n):\n",
    "                if not node in ant.path:\n",
    "                    rule = self.formun[len(ant.path)][node] * ((1/self.matrix[len(ant.path)][node])**self.beta)\n",
    "                    if rule > maxx:\n",
    "                        maxx = rule\n",
    "                        selected = node\n",
    "            return selected\n",
    "\n",
    "        else:\n",
    "            prob = [0 for i in range(self.n)]\n",
    "            summ = 0\n",
    "            for node in range(self.n):\n",
    "                if not node in ant.path:\n",
    "                    summ += self.formun[len(ant.path)][node] * ((1/self.matrix[len(ant.path)][node])**self.beta)\n",
    "\n",
    "            for node in range(self.n):\n",
    "                if not node in ant.path:\n",
    "                    prob[node] = (self.formun[len(ant.path)][node] * ((1/self.matrix[len(ant.path)][node])**self.beta))/summ\n",
    "                    a = random.choices(list(range(self.n)), prob, k=1)\n",
    "            return a[0]\n",
    "\n",
    "    def move(self,ant):\n",
    "        next_node = self.select_next_node(ant)\n",
    "        ant.cost += self.matrix[len(ant.path)][next_node]\n",
    "        ant.path.append(next_node)\n",
    "\n",
    "                \n",
    "    def mutation(self,path):\n",
    "        point_1, point_2 = random.sample(range(self.n), 2)\n",
    "        path[point_1] , path[point_2] = path[point_2] , path[point_1]\n",
    "        return path\n",
    "    \n",
    "    def fitness(self,path):\n",
    "        cost = 0\n",
    "        for i in range(self.n):\n",
    "            cost += self.matrix[i][path[i]]\n",
    "        return cost "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "insured-idaho",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-12-01T14:43:08.240580Z",
     "start_time": "2021-12-01T14:42:52.307555Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "339\n",
      "[67, 11, 10, 74, 28, 44, 95, 56, 22, 43, 21, 30, 87, 97, 12, 84, 0, 60, 29, 26, 19, 72, 31, 24, 5, 9, 64, 88, 92, 46, 58, 77, 50, 98, 75, 35, 33, 55, 13, 71, 79, 18, 86, 48, 91, 3, 27, 99, 90, 25, 82, 45, 36, 20, 54, 89, 41, 37, 38, 52, 53, 14, 66, 59, 47, 65, 39, 62, 78, 7, 63, 94, 70, 23, 76, 4, 69, 42, 40, 68, 51, 80, 16, 17, 83, 8, 57, 49, 32, 73, 81, 6, 61, 1, 85, 2, 34, 96, 15, 93]\n"
     ]
    }
   ],
   "source": [
    "test = read_data(2)\n",
    "colony = Colony(ant_num = 20, \n",
    "                matrix = test ,\n",
    "                base_formun = 10, \n",
    "                T_SA = 1, \n",
    "                SA_rate = 0.9, \n",
    "                beta = 10, \n",
    "                p_local = 0.4, \n",
    "                alpha = 0.2,\n",
    "                q_0 = 0.7\n",
    "               )\n",
    "hist,his_path = colony.training(50)\n",
    "\n",
    "print(min(hist))\n",
    "print(his_path[hist.index(min(hist))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "experienced-festival",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-12-01T14:43:10.731730Z",
     "start_time": "2021-12-01T14:43:10.620557Z"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "339\n"
     ]
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15, 5))\n",
    "plt.grid(True)\n",
    "plt.plot(range(50), hist)\n",
    "plt.show()\n",
    "print(min(hist))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "remarkable-gilbert",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-12-01T14:43:41.395171Z",
     "start_time": "2021-12-01T14:43:41.377219Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26\n",
      "[3, 1, 2, 0]\n"
     ]
    }
   ],
   "source": [
    "test = read_data(1)\n",
    "colony = Colony(ant_num = 5, \n",
    "                matrix = test ,\n",
    "                base_formun = 10, \n",
    "                T_SA = 1, \n",
    "                SA_rate = 0.9, \n",
    "                beta = 10, \n",
    "                p_local = 0.4, \n",
    "                alpha = 0.2,\n",
    "                q_0 = 0.7)\n",
    "hist,his_path = colony.training(20)\n",
    "print(min(hist))\n",
    "print(his_path[hist.index(min(hist))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "greenhouse-depth",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-12-01T14:51:30.537391Z",
     "start_time": "2021-12-01T14:49:24.081646Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "711\n",
      "[10, 27, 93, 176, 14, 104, 129, 15, 21, 40, 110, 68, 158, 195, 115, 66, 83, 11, 169, 42, 77, 45, 140, 106, 41, 149, 154, 3, 94, 87, 147, 16, 175, 23, 179, 161, 30, 142, 146, 170, 159, 32, 183, 56, 34, 58, 18, 160, 92, 148, 72, 132, 112, 90, 33, 39, 122, 128, 166, 20, 187, 85, 116, 52, 62, 165, 131, 55, 114, 44, 103, 73, 64, 48, 194, 82, 86, 31, 75, 4, 101, 196, 108, 168, 50, 190, 1, 28, 65, 107, 12, 198, 199, 181, 38, 46, 76, 178, 80, 189, 102, 25, 51, 35, 136, 177, 182, 63, 138, 78, 29, 113, 84, 24, 120, 88, 172, 19, 156, 81, 47, 191, 37, 192, 126, 134, 135, 5, 193, 174, 71, 143, 67, 2, 117, 57, 173, 36, 144, 145, 17, 7, 186, 0, 137, 184, 98, 125, 22, 8, 157, 89, 163, 69, 171, 162, 141, 139, 118, 49, 127, 111, 97, 121, 43, 119, 124, 79, 150, 99, 155, 123, 53, 197, 96, 70, 6, 105, 91, 9, 151, 61, 109, 167, 185, 130, 152, 100, 164, 188, 74, 54, 13, 59, 95, 26, 180, 153, 133, 60]\n"
     ]
    }
   ],
   "source": [
    "test = read_data(3)\n",
    "colony = Colony(ant_num = 20, \n",
    "                matrix = test ,\n",
    "                base_formun = 10, \n",
    "                T_SA = 10, \n",
    "                SA_rate = 0.9, \n",
    "                beta = 10, \n",
    "                p_local = 0.4, \n",
    "                alpha = 0.2,\n",
    "                q_0 = 0.7)\n",
    "hist,his_path = colony.training(50)\n",
    "print(min(hist))\n",
    "print(his_path[hist.index(min(hist))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "silver-parks",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-12-01T14:57:09.336528Z",
     "start_time": "2021-12-01T14:54:30.731648Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "744\n",
      "[59, 27, 93, 176, 14, 104, 122, 15, 21, 40, 110, 128, 158, 195, 115, 174, 83, 11, 173, 120, 77, 45, 140, 106, 48, 69, 125, 3, 94, 87, 147, 16, 175, 23, 179, 72, 30, 142, 146, 170, 159, 32, 183, 56, 34, 58, 18, 160, 92, 68, 178, 132, 112, 103, 33, 39, 13, 181, 51, 20, 187, 85, 116, 52, 62, 165, 131, 55, 114, 44, 188, 31, 64, 29, 194, 82, 86, 81, 88, 4, 101, 196, 108, 168, 78, 190, 73, 28, 65, 107, 12, 198, 154, 6, 38, 53, 166, 99, 80, 189, 102, 25, 61, 35, 136, 148, 182, 63, 138, 134, 139, 113, 84, 24, 150, 67, 172, 133, 156, 41, 47, 191, 37, 192, 126, 10, 137, 5, 193, 143, 71, 109, 149, 2, 117, 57, 79, 1, 144, 145, 17, 7, 186, 0, 135, 184, 98, 169, 22, 8, 157, 89, 19, 111, 36, 162, 46, 163, 118, 76, 127, 129, 90, 121, 43, 177, 171, 66, 54, 49, 155, 123, 42, 197, 96, 70, 50, 105, 91, 9, 151, 60, 124, 167, 185, 161, 152, 100, 164, 119, 74, 75, 141, 199, 95, 26, 180, 153, 97, 130]\n"
     ]
    }
   ],
   "source": [
    "test = read_data(3)\n",
    "colony = Colony(ant_num = 20, \n",
    "                matrix = test ,\n",
    "                base_formun = 10, \n",
    "                T_SA = 10, \n",
    "                SA_rate = 0.9, \n",
    "                beta = 10, \n",
    "                p_local = 0.4, \n",
    "                alpha = 0.2,\n",
    "                q_0 = 0.7)\n",
    "hist,his_path = colony.training(50)\n",
    "print(min(hist))\n",
    "print(his_path[hist.index(min(hist))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "expanded-forestry",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-12-01T14:53:01.365284Z",
     "start_time": "2021-12-01T14:53:00.939637Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "711\n"
     ]
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15, 5))\n",
    "plt.grid(True)\n",
    "plt.plot(range(50), hist)\n",
    "plt.show()\n",
    "print(min(hist))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "upper-brief",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.6"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "position": {
    "height": "101.85px",
    "left": "1255px",
    "right": "20px",
    "top": "81px",
    "width": "265px"
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
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   "window_display": true
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 "nbformat": 4,
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}