FIZ371_LN95_EST_SwarmOptimizations_Application_ArtificialBeeColony.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Swarm Optimizations Hands-on Applications\n",
    "**FIZ371 - Scientific & Technical Computations | 24/05/2021**\n",
    "\n",
    "* Ant Colony Optimization\n",
    "* **Artificial Bee Colony**\n",
    "\n",
    "Dr. Emre S. Tasci <emre.tasci@hacettepe.edu.tr>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fun(x):\n",
    "    return np.sin(x/2)/x +(-1)**np.round(x*1.25) * np.heaviside(x-2,1.5)*0.20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_min = -300\n",
    "x_max =  150\n",
    "x_delta = x_max - x_min\n",
    "neighbourhood = x_delta / 50\n",
    "\n",
    "x = np.linspace(x_min,x_max,500)\n",
    "y = fun(x)\n",
    "plt.plot(x,y,\"r-\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 100 # Number of bees\n",
    "\n",
    "# Distribute the bees randomly\n",
    "xs = x_min + np.random.rand(N) * x_delta\n",
    "ys = fun(xs)\n",
    "\n",
    "plt.plot(x,y,\"r-\")\n",
    "plt.plot(xs,ys,\"bo\",markersize=2.0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.23403234  0.21619961  0.21427205  0.2070134   0.20398596  0.2027654\n",
      "  0.19949759  0.19709637  0.19357576  0.19325441  0.19289011  0.19194718\n",
      "  0.18778734  0.18618732  0.18616816  0.18023746  0.17585771  0.09778483\n",
      "  0.02050297  0.0130822   0.0109774   0.00843695  0.00685335  0.00514947\n",
      "  0.004724    0.00469982  0.00469873  0.00447176  0.00435774  0.00426653\n",
      "  0.00392963  0.00379671  0.00369867  0.00365416  0.00359185  0.00338443\n",
      "  0.00327148  0.00305826  0.00249951  0.00235445  0.00162747  0.00123023\n",
      "  0.00084977  0.00079688  0.00041176 -0.00059877 -0.00094254 -0.00120771\n",
      " -0.00121273 -0.00126783 -0.00131753 -0.00148389 -0.00265707 -0.00293559\n",
      " -0.0031484  -0.00329939 -0.00353481 -0.00365446 -0.00413324 -0.00415531\n",
      " -0.00431399 -0.00461799 -0.00469219 -0.00480359 -0.00520632 -0.00548269\n",
      " -0.0059618  -0.0066515  -0.00805686 -0.00809671 -0.01190658 -0.01289675\n",
      " -0.01376936 -0.0211035  -0.02644335 -0.02862773 -0.02898479 -0.03876826\n",
      " -0.17253579 -0.18616343 -0.19224171 -0.19389895 -0.1967139  -0.19953619\n",
      " -0.20033357 -0.20105288 -0.20169992 -0.20275052 -0.20332085 -0.20357864\n",
      " -0.20376024 -0.2048857  -0.20622909 -0.20854071 -0.2100201  -0.21380483\n",
      " -0.22894125 -0.231256   -0.2910184  -0.29331719]\n"
     ]
    }
   ],
   "source": [
    "maxes = -np.sort(-ys)\n",
    "print(maxes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[127.88930082  30.38657935 105.94222971  65.22560969  28.70505025]\n"
     ]
    }
   ],
   "source": [
    "print(xs[ys > maxes[5]])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 4 super-sites and 6 meh sites.\n",
      "\n",
      "Number of followers allocated for the super sites: 35\n",
      "There will be 8 per super site, with an overhead of 3\n",
      "---------------------------------------------\n",
      "Number of followers allocated for the meh sites: 15\n",
      "There will be 2 per meh site, with an overhead of 3\n",
      "\n",
      "The range is [-300,150] and the neighbourhood is 9.00\n"
     ]
    }
   ],
   "source": [
    "n_scouts = int(np.ceil(N/10))\n",
    "n_supers = np.random.randint(n_scouts/2)+1\n",
    "n_mehs   = n_scouts - n_supers\n",
    "  \n",
    "n_super_followers = np.random.randint(N*0.25,N*0.40)\n",
    "n_meh_followers = int(N/2) - n_super_followers\n",
    "n_random_fliers = N - n_super_followers - n_meh_followers\n",
    "\n",
    "n_per_super_min = int(np.floor(n_super_followers/n_supers))\n",
    "n_super_plus = n_super_followers - n_per_super_min * n_supers\n",
    "\n",
    "n_per_meh_min = int(np.floor(n_meh_followers/n_mehs))\n",
    "n_meh_plus = n_meh_followers - n_per_meh_min * n_mehs\n",
    "\n",
    "\n",
    "print(\"There are {:} super-sites and {:} meh sites.\\n\"\\\n",
    "      .format(n_supers,n_mehs))\n",
    "print(\"Number of followers allocated for the super sites: {}\"\\\n",
    "     .format(n_super_followers))\n",
    "print(\"There will be {:} per super site, with an overhead of {:}\"\\\n",
    "     .format(n_per_super_min,n_super_plus))\n",
    "print(\"-\"*45)\n",
    "print(\"Number of followers allocated for the meh sites: {}\"\\\n",
    "     .format(n_meh_followers))\n",
    "print(\"There will be {:} per meh site, with an overhead of {:}\"\\\n",
    "     .format(n_per_meh_min,n_meh_plus))\n",
    "print(\"\\nThe range is [{:},{:}] and the neighbourhood is {:.2f}\"\\\n",
    "     .format(x_min,x_max,neighbourhood))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[127.88930082   0.2070134 ]\n",
      " [ 30.38657935   0.21619961]\n",
      " [ 65.22560969   0.21427205]\n",
      " [ 28.70505025   0.23403234]]\n",
      "[127.88930082  30.38657935  65.22560969  28.70505025] \n",
      " [0.2070134  0.21619961 0.21427205 0.23403234]\n",
      "---------------------------------------------\n",
      "[[147.44729461   0.19325441]\n",
      " [105.94222971   0.20398596]\n",
      " [100.43000739   0.19949759]\n",
      " [ 49.97424035   0.19709637]\n",
      " [ 56.23636856   0.2027654 ]\n",
      " [136.10188122   0.19357576]]\n",
      "[147.44729461 105.94222971 100.43000739  49.97424035  56.23636856\n",
      " 136.10188122] \n",
      " [0.19325441 0.20398596 0.19949759 0.19709637 0.2027654  0.19357576]\n",
      "[[147.44729461   0.19325441]\n",
      " [136.10188122   0.19357576]\n",
      " [ 49.97424035   0.19709637]\n",
      " [100.43000739   0.19949759]\n",
      " [ 56.23636856   0.2027654 ]\n",
      " [105.94222971   0.20398596]]\n"
     ]
    }
   ],
   "source": [
    "# supers\n",
    "filter_super = ys > maxes[n_supers]\n",
    "x_supers = xs[filter_super]\n",
    "y_supers = ys[filter_super]\n",
    "xy_supers = np.column_stack((x_supers.T,y_supers.T))\n",
    "print(xy_supers)\n",
    "print(x_supers,\"\\n\",y_supers)\n",
    "\n",
    "print(\"-\"*45)\n",
    "\n",
    "# mehs\n",
    "filter_meh = np.logical_and(ys > maxes[n_supers+n_mehs]\\\n",
    "                            ,ys <= maxes[n_supers])\n",
    "x_mehs = xs[filter_meh]\n",
    "y_mehs = ys[filter_meh]\n",
    "xy_mehs = np.column_stack((x_mehs,y_mehs))\n",
    "print(xy_mehs)\n",
    "print(x_mehs,\"\\n\",y_mehs)\n",
    "\n",
    "print(xy_mehs[xy_mehs[:,1].argsort()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[147.44729461,   0.19325441],\n",
       "       [105.94222971,   0.20398596],\n",
       "       [100.43000739,   0.19949759],\n",
       "       [ 49.97424035,   0.19709637],\n",
       "       [ 56.23636856,   0.2027654 ],\n",
       "       [136.10188122,   0.19357576]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xy_mehs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  4.        ,   5.        ],\n",
       "       [147.44729461,   0.19325441],\n",
       "       [105.94222971,   0.20398596],\n",
       "       [100.43000739,   0.19949759],\n",
       "       [ 49.97424035,   0.19709637],\n",
       "       [ 56.23636856,   0.2027654 ],\n",
       "       [136.10188122,   0.19357576]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.insert(xy_mehs,0,([4,5]),0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "5%3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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