Lab Ass 3.2.2 (PSO, Q2).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "enter the number of variables: 20\n",
      "Enter the initial population size: 10\n",
      "Enter the minimum value of X: -100\n",
      "Enter the maximum value of X: 100\n",
      "Enter the max number of iterations: 300\n",
      "[41922.00445896 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318 32868.99232318 32868.99232318 32868.99232318\n",
      " 32868.99232318]\n"
     ]
    },
    {
     "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": [
    "#Generating Initial Population\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "d=int(input(\"enter the number of variables: \"))\n",
    "N=int(input(\"Enter the initial population size: \"))\n",
    "xmin=float(input(\"Enter the minimum value of X: \"))\n",
    "xmax=float(input(\"Enter the maximum value of X: \"))\n",
    "iterations=int(input(\"Enter the max number of iterations: \"))\n",
    "def POP(d,N,xmin,xmax):\n",
    "    return ((np.random.rand(N,d))*(2*xmax))-xmax\n",
    "pop=POP(d,N,xmin,xmax)\n",
    "#intial setup\n",
    "import numpy as np\n",
    "def cfun(a):\n",
    "    return np.sum(a**2)\n",
    "\n",
    "def GBEST(pbest):\n",
    "    cost=np.apply_along_axis(cfun,1,pbest)\n",
    "    gbestcost=np.argmin(cost)\n",
    "    return np.array([pbest[gbestcost,:]])\n",
    "def setup(pop):\n",
    "    cost=np.apply_along_axis(cfun,1,pop)\n",
    "    pbest=np.copy(pop)\n",
    "    v=np.zeros((N,d))\n",
    "    gbest= GBEST(pbest)\n",
    "    GBests=np.zeros((iterations+1,d))\n",
    "    GBests[0]=gbest\n",
    "    return cost, pbest, v, gbest, GBests\n",
    "cost, pbest, v, gbest, GBests= setup(pop)\n",
    "#updating v,x,pbest,gbest\n",
    "for x in range(0,iterations):\n",
    "    for i in range(0,N):\n",
    "        for j in range(0,d):\n",
    "            v[i,j]+=(2*(np.random.rand())*(pbest[i,j]-pop[i,j]))+(2*(np.random.rand())*(gbest[0,j]-pop[i,j]))\n",
    "            z=pop[i,j]+v[i,j]\n",
    "            if z<=xmin:\n",
    "                pop[i,j]=xmin\n",
    "            elif z>=xmax:\n",
    "                pop[i,j]=xmax\n",
    "            else:\n",
    "                pop[i,j]=z\n",
    "        if cfun(pop[i])<cfun(pbest[i]):\n",
    "            pbest[i]=pop[i]\n",
    "    gbest= GBEST(pbest)\n",
    "    GBests[x+1]=gbest\n",
    "GBestscost=np.apply_along_axis(cfun,1,np.array(GBests))\n",
    "print(GBestscost)\n",
    "plt.plot(GBestscost)\n",
    "plt.xlabel('Iterations')\n",
    "plt.ylabel('Gbest Cost')\n",
    "plt.title('PSO')\n",
    "plt.show()"
   ]
  }
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