Search method of bees algorithm

bees.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def func(bee):\n",
    "    return np.sum(np.array(bee.vector)**2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Bee(object):\n",
    "    def __init__(self, vector, fitness=None):\n",
    "        self.vector = vector\n",
    "        self.fitness = fitness if fitness else func(self)\n",
    "\n",
    "    def __lt__(self, other):\n",
    "        return self.fitness < other.fitness\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def create_neigh_bee(parent, patch_size, lower_bound, upper_bound):\n",
    "    def rand():\n",
    "        r = np.random.uniform()\n",
    "        if r < 0.5: return -r\n",
    "        return r\n",
    "\n",
    "    vector = np.array([v + rand()*patch_size for v in parent.vector])\n",
    "    vector[vector > upper_bound] = upper_bound\n",
    "    vector[vector > lower_bound] = lower_bound\n",
    "    return Bee(vector=vector)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def search_neigh(parent, neigh_size, patch_size, lower_bound, upper_bound):\n",
    "    neigh = []\n",
    "    for i in range(neigh_size): neigh.append(create_neigh_bee(parent, patch_size, lower_bound, upper_bound))\n",
    "    return sorted(neigh)[0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def search(lower_bound, upper_bound, max_gens, problem_size, patch_size, bees_num,\n",
    "           sites_num, elite_sites_num, elite_bees_num, other_bees_num):\n",
    "    best = None\n",
    "    pop = []\n",
    "    for i in range(bees_num): pop.append(Bee(vector=np.random.uniform(lower_bound, upper_bound, problem_size)))\n",
    "    for g in range(max_gens):\n",
    "        pop = sorted(pop)\n",
    "        best = pop[0] if not best or pop[0].fitness < best.fitness else best\n",
    "        next_gen = []\n",
    "        for idx, parent in enumerate(pop[:sites_num]):\n",
    "            neigh_size = elite_bees_num if idx < elite_sites_num else other_bees_num\n",
    "            next_gen.append(search_neigh(parent, neigh_size, patch_size, lower_bound, upper_bound))\n",
    "        scouts = []\n",
    "        for s in range(bees_num-sites_num):\n",
    "            scouts.append(Bee(vector=np.random.uniform(lower_bound, upper_bound, problem_size)))\n",
    "        pop = next_gen + scouts\n",
    "        patch_size *= 0.95\n",
    "        print('it=#{}, patch_size=#{}, worse=#{}, f=#{}'.format(g, patch_size, pop[-1].fitness, best.fitness))\n",
    "    return best\n"
   ]
  },
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      "it=#470, patch_size=#9.659159682643832e-11, worse=#5.665145562548545, f=#0.12238320326037948\n",
      "it=#471, patch_size=#9.17620169851164e-11, worse=#25.126876854515956, f=#0.12238320326037948\n",
      "it=#472, patch_size=#8.717391613586058e-11, worse=#15.226595423979983, f=#0.12238320326037948\n",
      "it=#473, patch_size=#8.281522032906755e-11, worse=#17.70017315998358, f=#0.12238320326037948\n",
      "it=#474, patch_size=#7.867445931261418e-11, worse=#30.042998718723094, f=#0.12238320326037948\n",
      "it=#475, patch_size=#7.474073634698346e-11, worse=#8.285402451039626, f=#0.12238320326037948\n",
      "it=#476, patch_size=#7.100369952963428e-11, worse=#18.26380732959644, f=#0.12238320326037948\n",
      "it=#477, patch_size=#6.745351455315256e-11, worse=#16.78969776857086, f=#0.12238320326037948\n",
      "it=#478, patch_size=#6.408083882549493e-11, worse=#18.70090164069565, f=#0.12238320326037948\n",
      "it=#479, patch_size=#6.087679688422018e-11, worse=#56.75103189979309, f=#0.12238320326037948\n",
      "it=#480, patch_size=#5.783295704000917e-11, worse=#21.99827413504976, f=#0.12238320326037948\n",
      "it=#481, patch_size=#5.494130918800871e-11, worse=#43.421434077234544, f=#0.12238320326037948\n",
      "it=#482, patch_size=#5.2194243728608274e-11, worse=#24.984037535034066, f=#0.12238320326037948\n",
      "it=#483, patch_size=#4.9584531542177856e-11, worse=#14.014221017899443, f=#0.12238320326037948\n",
      "it=#484, patch_size=#4.710530496506896e-11, worse=#33.671785588100974, f=#0.12238320326037948\n",
      "it=#485, patch_size=#4.475003971681551e-11, worse=#46.31850304909327, f=#0.12238320326037948\n",
      "it=#486, patch_size=#4.251253773097473e-11, worse=#12.135412805862014, f=#0.12238320326037948\n",
      "it=#487, patch_size=#4.038691084442599e-11, worse=#44.491964790663005, f=#0.12238320326037948\n",
      "it=#488, patch_size=#3.8367565302204694e-11, worse=#20.600895622510805, f=#0.12238320326037948\n",
      "it=#489, patch_size=#3.644918703709446e-11, worse=#29.559807934297837, f=#0.12238320326037948\n",
      "it=#490, patch_size=#3.462672768523973e-11, worse=#24.205539129323817, f=#0.12238320326037948\n",
      "it=#491, patch_size=#3.289539130097774e-11, worse=#14.727746579291349, f=#0.12238320326037948\n",
      "it=#492, patch_size=#3.125062173592886e-11, worse=#27.366743066854077, f=#0.12238320326037948\n",
      "it=#493, patch_size=#2.9688090649132415e-11, worse=#21.894881713313833, f=#0.12238320326037948\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it=#494, patch_size=#2.8203686116675792e-11, worse=#42.30516493329152, f=#0.12238320326037948\n",
      "it=#495, patch_size=#2.6793501810842002e-11, worse=#4.286297140305528, f=#0.12238320326037948\n",
      "it=#496, patch_size=#2.54538267202999e-11, worse=#26.497113803016404, f=#0.12238320326037948\n",
      "it=#497, patch_size=#2.4181135384284903e-11, worse=#16.124659440554165, f=#0.12238320326037948\n",
      "it=#498, patch_size=#2.2972078615070656e-11, worse=#41.2274083176176, f=#0.12238320326037948\n",
      "it=#499, patch_size=#2.1823474684317122e-11, worse=#23.851893836340352, f=#0.12238320326037948\n",
      "Solution: f=#0.12238320326037948, s=#[ 0.18102855  0.07173055  0.29063137]\n"
     ]
    }
   ],
   "source": [
    "if __name__ == '__main__':\n",
    "    problem_size = 3\n",
    "    max_gens = 500\n",
    "    bees_num = 45\n",
    "    sites_num = 3\n",
    "    elite_sites_num = 1\n",
    "    patch_size = 3.0\n",
    "    elite_bees_num = 7\n",
    "    other_bees_num = 2\n",
    "    best = search(-5, 5, max_gens, problem_size, patch_size, bees_num, \n",
    "                 sites_num, elite_sites_num, elite_bees_num, other_bees_num)\n",
    "    print('Solution: f=#{}, s=#{}'.format(best.fitness, best.vector))\n",
    "    "
   ]
  }
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