example of constrained PSO.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from psoclasses.consoptipso import consadawpsoaeq\n",
    "from psoclasses.consoptipso import consadawpso\n",
    "#求解问题为极小形\n",
    "#必须将Aeq,A写成 AX-B<=0或AX-B=0的形式\n",
    "#the maxv parameter needs to be chosen carefully and affects the results very much\n",
    "# Too large leads to oscillation, too small is hard to find the optimal value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "总耗时：0.2649576663970947s\n",
      "最优变量:[2.5 2.5]\n",
      "最优值是:12.500000\n",
      "总耗时：0.21554899215698242s\n",
      "最优变量:[2. 3.]\n",
      "最优值是:12.000000\n",
      "总耗时：0.2242145538330078s\n",
      "最优变量:[2.75 2.  ]\n",
      "最优值是:12.250000\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import time\n",
    "#如果求解最大值必须将函数乘以-1，之后令sovmax=True\n",
    "upper=[30,30]\n",
    "lower=[0,0]\n",
    "isints=[0,0]\n",
    "def aeqf(X):#不满足约束统一计算，返回的数组是0或满足\n",
    "    eq1=4*X[:,0]+2*X[:,1]-15\n",
    "    eq2=X[:,0]+2*X[:,1]-8\n",
    "    eq3=X[:,0]+X[:,1]-5\n",
    "    result=np.zeros(X.shape[0])\n",
    "    for iter in range(X.shape[0]):#如果满足约束,此时所有变量应小于等于0\n",
    "        if  eq1[iter]<=0 and eq2[iter]<=0 and eq3[iter]<=0:\n",
    "            result[iter]=np.zeros(1)\n",
    "        else:#如果满足约束\n",
    "            result[iter]=np.ones(1)\n",
    "    \n",
    "    return result\n",
    "dim=2\n",
    "psize=200\n",
    "iter_num=1000\n",
    "def fun1s(X):\n",
    "    z=3*X[:,0]+2*X[:,1]\n",
    "    return -1*z\n",
    "pso=consadawpsoaeq(fun1s,lower,upper,dim,psize,isints,aeqf,max_v=2,ifplot=True,sovmax=True)\n",
    "starttime=time.time()\n",
    "pso.fit()\n",
    "endtime=time.time()\n",
    "print(f\"总耗时：{endtime-starttime}s\")\n",
    "pso.printresult()\n",
    "isints=[1,0]#x is integer\n",
    "pso=consadawpsoaeq(fun1s,lower,upper,dim,psize,isints,aeqf,max_v=2,ifplot=True,sovmax=True)\n",
    "starttime=time.time()\n",
    "pso.fit()\n",
    "endtime=time.time()\n",
    "print(f\"总耗时：{endtime-starttime}s\")\n",
    "pso.printresult()\n",
    "isints=[0,1]#y is integer\n",
    "pso=consadawpsoaeq(fun1s,lower,upper,dim,psize,isints,aeqf,max_v=2,ifplot=True,sovmax=True)\n",
    "starttime=time.time()\n",
    "pso.fit()\n",
    "endtime=time.time()\n",
    "print(f\"总耗时：{endtime-starttime}s\")\n",
    "pso.printresult()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "总耗时：0.13327336311340332s\n",
      "最优变量:[0.78867234 0.40825619]\n",
      "最优值是:263.895843\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import time\n",
    "#如果求解最大值必须将函数乘以-1，之后令sovmax=True\n",
    "def fun1s(X):\n",
    "    L=100\n",
    "    x1=X[:,0]\n",
    "    x2=X[:,1]\n",
    "    z=(2*np.sqrt(2)*x1+x2)*L\n",
    "    return z\n",
    "\n",
    "def aeqf(X):#不满足约束统一计算，返回的数组是0或满足\n",
    "    P=2\n",
    "    sigma=2\n",
    "    x1=X[:,0]\n",
    "    x2=X[:,1]\n",
    "    eq1=P*(np.sqrt(2)*x1+x2)/(np.sqrt(2)*(x1**2)+2*x1*x2)-sigma\n",
    "    eq2=P*(x2)/(np.sqrt(2)*(x1**2)+2*x1*x2)-sigma\n",
    "    eq3=P*(1)/(np.sqrt(2)*(x2)+x1)-sigma\n",
    "    result=np.zeros(X.shape[0])\n",
    "    for iter in range(X.shape[0]):#如果满足约束,此时所有变量应小于等于0\n",
    "        if  eq1[iter]<=0 and eq2[iter]<=0 and eq3[iter]<=0:\n",
    "            result[iter]=np.zeros(1)\n",
    "        else:#如果满足约束\n",
    "            result[iter]=np.ones(1)\n",
    "    \n",
    "    return result\n",
    "dim=2\n",
    "psize=100\n",
    "iter_num=1000\n",
    "upper=[1,1]\n",
    "lower=[0.001,0.001]\n",
    "isints=[0,0]\n",
    "pso=consadawpsoaeq(fun1s,lower,upper,dim,psize,isints,aeqf,max_v=1,ifplot=True,sovmax=False)\n",
    "starttime=time.time()\n",
    "pso.fit()\n",
    "endtime=time.time()\n",
    "print(f\"总耗时：{endtime-starttime}s\")\n",
    "pso.printresult()\n",
    "result=pso.getresult()#get x y\n",
    "resultx=result[0]\n",
    "#print(aeqf(resultx.reshape(1,resultx.shape[0])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "总耗时：0.6384057998657227s\n",
      "最优变量:[ 0.60110646  0.29712723 31.14541237 10.        ]\n",
      "最优值是:863.729823\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEGCAYAAACJnEVTAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAX4klEQVR4nO3df4xlZ33f8ff33jszuzOzu+yP2cXGBGNsx0GA+bGA1zFmbX7VQUTFrQQqEFpCrVISiSAijCBqmgYVJSihQqXIiqGo6g8bpbgFEjCJABvHRp0Njgs4rnFsAwazY7ze2dnfM/PtH+fcnTu7szuzM565y3neL2k95z7n3HOf88jzOc885znnRmYiSSpDq98VkCStHUNfkgpi6EtSQQx9SSqIoS9JBTH0VZyodPpdD6kfDH01WkS8IiKeWy+PRsQfABuAidNs34mI/xkRG+rtP1WXb11g21+NiOtOs5/PRsQNC5Svi4jXR8SrVnJc0nIZ+mq6fwJ8qF6+CvgV4CDw1Gm2vxI4PzMPAEeBd9Xl/yUi3nPStgPA5yJixwL7mQKOd19ExNaI+D/Ag8BHgcsi4pqIeDIi7q3/fS8ifnr2hygtXXhzlposIs4H7gIuBf6Y6iTwc+Ay4O+BYeBjmfmZevvPAt/KzJvr1weAzcDFwHsz87dP2v8fA18H/hnVCaX7C/Vs4AhwGHhjZj5Ub/9W4LLM/P2IuBK4ITP/eb3uAuCuzHzOKjSFBBj6KkBEDAMzwMNUYX8MuCczX1yvb2fmTEQ8C3gIuCIz763XPQq8jOrkcCUw3D1BLPA5mzJzf738CeDezPzP9evnAB8AngdsBf4K+Brwv4Bu774DjGTmeU/j4UvzeDFLjVWPw19A1eP+AXB1Zk5GxDOBfT2bdqhOCh8HAhiue+QvAjYCdwAPAHuAb9X7vgq4ieoE8kGqvyZ+EBGXZ+ZPFqjOPuDPgfcB3wf+IjPvAjY9nccsLcbQV5O9EPht4JXAbwG/HxEzwDOATRExThXyQxHxb4BfAv4amAZeDNxN9ZfBLZl5y0n7/pt6/zdT9f6nIuIrwO8Av3tyReqTzXeAVwH/HdgSEf8K+JdUJ6V1VENBG4B3Zeaep6sRpF6GvhorM78BfCMifpyZtwG3AUTEzcCrqcL1vrpsmCrkbwKOZeaNdfk24FrglpP2PVuvh7lx/I8C289QpU8AP6T66+CPgOsy89MRcTHwZ5m5eyXHKy2Fs3dUlIh4C/B84E3ALRFxLUBmHjrNsMwXgTdFxLrF9p2Zfw98KyJGgPOoZv90P/ftwC7gT4FJqhPJhoj4f8AXgJdFxN9GxN0rOkBpEYa+ilDPj/8j4MPAmzPzfuAfU03FfNPp3peZe6mGfD5Q7+fVEbGhXh4BLqca++8aBh4HfpnqWkDXF4F3UvXyycyfAt+jmvHzZmBPZr40M3et/Gil03N4R40WEb8LPJPqIu0U8KruDJvMfCAirgcei4hOZk5T/U60T9rNB4C7IuIw8B7gHRHxt8D/rrf/rYj4ZeA/Ul3s3ZiZGRFDEXERMJOZjwLfrody2vXn9w4RSWvCnr4aKyJeC7yBap79t6kuon43Ih6OiH+IiB8Bf0E1lfMV9dsGgMHe/WTmz4DXANfU295DdXE4qKZzXgI8AvwZ1Z2++yLiCNUF2oeo5ux3DQKDEXFBRNwfEXuoZvU8PyLGI+LvIuI/PL0tIc1xnr4aracH/wu1b2m1GPqSVBCHdySpIIa+JBXknJ69s23btrzwwgv7XQ1J+oWyZ8+eJzJzbKF153ToX3jhhYyPj/e7GpL0C6V+UOCCHN6RpIIY+pJUEENfkgpi6EtSQQx9SSqIoS9JBTH0Jakg5/Q8/eV64PEDfPm+n9BqBf/0ZRdwwebhfldJks4JjQz9H+yd4pNf/wGZMJvw/tdd2u8qSdI5oZHDO2980Xk8/O/fyGCnxbHp2X5XR5LOGY0M/a6BVjA9Y+hLUlejQ7/TbjE96/cFSFJXo0N/oB0ct6cvSSc0OvQ7rRbTM/b0Jamr2aHfDo7P2tOXpK5Gh/5A256+JPVqdOh3Wo7pS1KvZod+u8Vxe/qSdEKjQ3+gHUw7pi9JJzQ69DutcExfkno0O/TbLcf0JalHo0O/Gt6xpy9JXY0O/ermLHv6ktTV6NCvHsNgT1+Suhod+p1Wy9k7ktSj2aHfdvaOJPVqdOgPtFs+e0eSejQ69J2nL0nzNTv0fQyDJM3T6ND3MQySNF+jQ98vUZGk+Rod+n5doiTN1+jQ7/gYBkmap9mh32oxM5tkGvySBEsM/YjYERHfqZdvjoi7I+IjPeuXXbaaBtoB4AweSaottaf/cWB9RFwPtDNzF3BRRFyykrLVOKBenXZ1eM7gkaRKZ7ENIuJa4CDwOLAbuLVedTtwFfCSFZQ9uNIDOJNOy56+JPU6Y08/IgaB3wNurItGgMfq5SeBHSssW+gzb4iI8YgYn5iYONvjmWeg29N3Bo8kAYsP79wIfCozn6pfTwHr6+XR+v0rKTtFZt6UmTszc+fY2NhZHczJOvWYvjN4JKmyWOi/FnhvRHwDeDHwJqphGYDLgUeAPSsoW1UDrerwnKsvSZUzjuln5tXd5Tr4fx24MyLOB64DrgByBWWr6kRP3zF9SQLOYp5+Zu7OzEmqi7n3ANdk5v6VlD2dB7IQZ+9I0nyLzt45WWbuY24WzorLVtOAs3ckaZ5G35HbqkN/xgu5kgQ0PPS78/RnfQyDJAEND/1uT98pm5JUaXTot6Pu6Rv6kgQ0PfQd05ekeRod+q26pz/jmL4kAQ0P/e7NWU7Tl6RKo0O/29P35ixJqjQ69NtO2ZSkeZod+t0xfTv6kgQ0PPTrh2w6e0eSao0O/U6d+g7vSFKl0aFfP2TTO3Ilqdbo0G95R64kzdPo0PeOXEmar4zQd0xfkoBCQt/hHUmqNDv0w0crS1KvRod+yztyJWmeRof+3B25hr4kQdNDv23oS1KvZod+OLwjSb2aHfp+R64kzdPo0PeOXEmar9Gh32n5aGVJ6tXo0G95R64kzdPo0IdqXN/hHUmqND/0I7yQK0m1xod+q+WUTUnqanzod1otb86SpFrjQ78V3pErSV2ND/12KxzekaRaEaHvhVxJqiwp9CNiS0S8LiK2rXaFnm6tcMqmJHUtGvoRsRn4EvAK4OsRMRYRN0fE3RHxkZ7tll22mjqtcExfkmpL6em/CHh/Zn4U+CpwLdDOzF3ARRFxSURcv9yy1TmsOa1WeEeuJNU6i22Qmd8EiIirqXr7W4Bb69W3A1cBL1lB2YMrPYgz8Y5cSZqz1DH9AN4C7AMSeKxe9SSwAxhZQdnJn3VDRIxHxPjExMTZHs8pvCNXkuYsKfSz8l7gPuBKYH29arTex9QKyk7+rJsyc2dm7hwbGzvrAzqZUzYlac5SLuR+MCJ+o375DOBjVMMyAJcDjwB7VlC2qtpeyJWkExYd0wduAm6NiHcD3wVuA+6IiPOB64ArqIZ87lxm2apqRfg8fUmqLdrTz8x9mfm6zLw6M/91Zu4HdgP3ANdk5v7MnFxu2WocVC+HdyRpzlJ6+qfIzH3MzcJZcdlqanlHriSd0PjHMHScsilJJzQ+9NvhhVxJ6mp86LdafkeuJHU1PvS9I1eS5jQ+9FvekStJJzQ+9DtO2ZSkExof+t6RK0lzGh/6LWfvSNIJjQ9978iVpDlFhL4XciWp0vjQ3zY6xOP7jzhtU5IoIPSff95GDh2b4dEnD/W7KpLUd80P/fM3AvDX9/+MBx4/4EVdSUVrfOhfvH2UoU6LP/zy/bzhE3fw6W8+1O8qSVLfND701w20+fP3XMmn3/5SRoc67J080u8qSVLfND70AV7wrE38oxecx6b1Axw8NtPv6khS3xQR+l3Dg20OHZvudzUkqW/KCv2hDgeP2tOXVK6iQn/Enr6kwhUV+sOD9vQlla2o0B8ZsqcvqWxFhf7wYMfZO5KKVlTojwy2OXTUnr6kchUV+sNDHQ4dn/Hha5KKVVTojwy2yYQj0w7xSCpTUaE/PNQBcAaPpGIVFfojg20AZ/BIKlZRoT88WPX0p7yYK6lQRYX++rqnf+S4wzuSylRU6K/rVId75Phsn2siSf1RVOh3e/qHvUFLUqGKCv11A/XwjlM2JRWqrNDvdMf0Hd6RVKayQn+wOtzDXsiVVKhFQz8iNkXEX0bE7RHxhYgYjIibI+LuiPhIz3bLLlsr3eGdo4a+pEItpaf/NuBPMvP1wOPAW4F2Zu4CLoqISyLi+uWWrc5hLWxueMfQl1SmzmIbZOanel6OAW8HPlG/vh24CngJcOsyyx5cdu3P0kA7aLfC4R1JxVrymH5E7AI2Az8CHquLnwR2ACMrKDv5c26IiPGIGJ+YmDirg1nCMbCu0/JCrqRiLSn0I2IL8EngXcAUsL5eNVrvYyVl82TmTZm5MzN3jo2Nne3xLGrdQNvhHUnFWsqF3EHg88CHMvNRYA/VsAzA5cAjKyxbU+sG2g7vSCrWomP6wG8CLwU+HBEfBj4LvCMizgeuA64AErhzmWVrat1Ai6MO70gq1KI9/cz8T5m5OTN31/8+B+wG7gGuycz9mTm53LLVOKgzcXhHUsmW0tM/RWbuY24WzorL1tJ6h3ckFayoO3LBnr6kshUZ+ocd05dUqAJDv8WP9x3iD774fR+xLKk4xYX+rudtZf1Am8/c9TBf+d5P+10dSVpTxYX+2175HO750GvYvmGIf/el+3nsqcP9rpIkrZniQh+g1Qquf+kFPHnwGJ/7m0f6XR1JWjNFhj7AjdddVt+o5bi+pHIUG/oAo0Mdpmez39WQpDVTdOi3W8GMoS+pIEWHfqfVsqcvqShFh367FUzPeKOWpHIUHfqdVtjTl1SUskO/7Zi+pLIUHfptx/QlFabo0O84e0dSYYoO/XYrOO6FXEkFKTr07elLKk3Zod929o6kspQd+q2WPX1JRSk69NvO05dUmKJDv+MduZIKU3To+8A1SaUpOvQH2t6cJaksRYe+PX1JpSk69KsHrjmmL6kcRYd+9Whle/qSylF06HtzlqTSlB363pwlqTBFh77fnCWpNEWHvg9ck1SaokO/3Q6OG/qSClJ06NvTl1SawkO/upCbafBLKkPhoR8A9vYlFaPo0G+3q9B3rr6kUiwp9CNiR0Tc2fP65oi4OyI+8nSU9Uu3p2/oSyrFoqEfEZuBzwEj9evrgXZm7gIuiohLVlK2Wge2FO1WdfgzPopBUiGW0tOfAd4CTNavdwO31su3A1etsGyeiLghIsYjYnxiYmLJB7IcAyeGd7xBS1IZFg39zJzMzP09RSPAY/Xyk8COFZad/Hk3ZebOzNw5NjZ2dkdzltpeyJVUmOVcyJ0C1tfLo/U+VlLWN47pSyrNckJ3D3PDMpcDj6ywrG+6Y/o+XllSKTrLeM9twJ0RcT5wHXAFkCso65u5nr5j+pLKsOSefmburn9OUl2QvQe4JjP3r6TsaTuSZei0HdOXVJbl9PTJzH3MzcJZcVm/OKYvqTRl35Hbnadv6EsqRNGh3+3pf/z2B/pcE0laG0WH/guetQmAbz34hL19SUUoOvTHNgzxsetfyPRs8ti+w/2ujiStuqJDH+CisVEAHnpiqs81kaTVZ+iPjQDwX+/5oV+mIqnxig/9rSODRMBf3f8z/u7Hfb1tQJJWXfGhHxF85p0vB2DfwWN9ro0kra7iQx/ggs3VM+Cmjk73uSaStLoMfWB4qLox+dAxQ19Ssxn6wOhgFfpTR2f6XBNJWl2GPjA81AbgoMM7khrO0AcG2i0GOy0OOrwjqeEM/droUMeevqTGM/Rrw4NtDjqmL6nhDP2aPX1JJTD0ayNDHcf0JTWeoV8bHmw7ZVNS4xn6tdGhDocc3pHUcIZ+bXiww4N7p/jGA3v7XRVJWjWGfu2NL3omAJ8f/3GfayJJq8fQr1172Q5ec9l2frDXL1OR1FyGfo+Lt4/y8BMHmZ6Z7XdVJGlVdPpdgXPJ87aPcmxmln/7xe+feB5P18hgh3fuupBNwwN9qp0krZyh3+PlF25h8/AAt47/6JR1R6dn+W/f/iHbNgwua9+vvnSM33ntpXTa/nElqX/iXP5e2J07d+b4+Hi/qwHA1x/Yu+zv0Z06Os23H36SZwwPsHl4kDe/5Fm85eXPZsfGdatQU0mli4g9mblzwXWG/tr46vce52vf/xkPTUzxnR8+xfmb1nHjr/0KscqfGwG7LtrK1tGhVf4kSecKQ/8cMjubfOV7j/O+/3Evx9bogvGGdZ0F/6oYGWzzL371uWwZObshq1YEz9w0RCuqU1bE3Kmru9RTRBDzytYNtNm4vnPi/afTacW8fUtamjOFvmP6a6zVCn7thefxyuduYd+h1f8i9p9PHeOW8R9x9PipJ5j/+9h+3nfLvateh+W6dMcoVz5v27yyiPknkd6TTPcEESf+U20bJ5bpWZ5fTu97l/g5vdu1W9XJsPpXbdOqP7BVb9OKahvqnwG0WnN16b6nHUG7FXTa1f46rRatnv23W91t5/YZ9c+BdouN6ztsXDfAUKflSVOnMPT7ZOvo0JoMuVy8HV550dYF1x0+NsP3fzoJnN1fe8emk70HjgDQ+4di1vuZV5bddd3XyZHjM0wemT7j9ZHp2eRL9/2UL3znMTJzroY5f19zy3N1mPeZpynvfnaeVN8mGWy32LCuw5aRQXZsXMf2DUOMbRzivI3r2F6/3r5hHWMbhlg/2F58h2oEQ79g6wfbvOw5m/tdjdN632svXfPPPHEyWOQEMe8kkjCTycxsVieihNn6hDSb1VlqNqv3zGY1xEe9rnfbzGr9zGye+Dc9m8xmMj2TJ9bPZs57b7csMzk6PcvkkWkmDx/nwJFpJo8c5+dTR9l74CjffvggEweOLjisuGGow+aRQTatHzjxb2P98xnDA2wdGWTbhiHGRofYvmGILSODzkT7BWXoSz1ODBHNGxVpzhDJ7Gzy5KFj7J08ysTUUfZOHql/HuWpQ8d46vBx9h8+zk/2H2ayXj4+c+qfQhFUJ4LRIcY29Pzreb19wxBjo+vYuL7jMNM5xNCXCtJqBdtGh9i2xKHFzOTQsRl+PnWMvQeO8MTUUSYOHGVi6lj180B18viHidP/FTHYbrF5ZIB2VBfmq+sXc9c1lntCWPZpZAXnn+W+dTnH+NaXP5t3v+qiZX7i6Rn6kk4rIhgZ6jAy1OGXtg6fcdvMZPLwNBNTR9jbPSHUJ4V9B49VQ1w5NzTWHa5ajuVehlnJbMVlv3OZb1zqifls9SX0I+Jm4PnAlzPzD/tRB0lPr4hg0/AAm4YHuHj7hn5XR6ex5ldiIuJ6oJ2Zu4CLIuKSta6DJJWqH5ffdwO31su3A1f1oQ6SVKR+hP4I8Fi9/CSwo3dlRNwQEeMRMT4xMbHmlZOkJutH6E8B6+vl0ZPrkJk3ZebOzNw5Nja25pWTpCbrR+jvYW5I53LgkT7UQZKK1I/ZO7cBd0bE+cB1wBV9qIMkFWnNe/qZOUl1Mfce4JrM3L/WdZCkUvVlnn5m7mNuBo8kaY2c08/Tj4gJ4NFlvn0b8MTTWJ1fZLbFHNtiPttjTpPa4jmZueBMmHM69FciIsZP9yUCpbEt5tgW89kec0ppC5+NKkkFMfQlqSBNDv2b+l2Bc4htMce2mM/2mFNEWzR2TF+SdKom9/QlARGxJSJeFxHbFt9aTdfI0I+ImyPi7oj4SL/rstYiYkdE3Nnz+pS2aHr7RMSmiPjLiLg9Ir4QEYMltgNARGwGvgS8Avh6RIyV2hZd9e/Id+rl4tqicaFf8vP661/wz1E9yXTBtiikfd4G/Elmvh54HHgrZbYDwIuA92fmR4GvAtdSblt0fRxYX+rvR+NCn7Kf1z8DvAWYrF/v5tS2WKisUTLzU5n5tfrlGPB2CmwHgMz8ZmbeExFXU/X230ChbQEQEdcCB6k6A7spsC2aGPpnfF5/k2Xm5EnPMlqoLYppn4jYBWwGfkTZ7RBUnYF9VN/YWmRbRMQg8HvAjXVRkb8fTQz9Mz6vvzALtUUR7RMRW4BPAu+i4HYAyMp7gfuAKym3LW4EPpWZT9Wvi/z/onEHhM/r77VQWzS+feoe3eeBD2XmoxTaDgAR8cGI+I365TOAj1FoWwCvBd4bEd8AXgy8iQLboi9P2Vxlt+Hz+rtu49S2yAXKmuY3gZcCH46IDwOfBd5RYDtAdcPRrRHxbuC7VP9P3FFiW2Tm1d3lOvh/nQJ/Pxp5c1Y9i+V1wB2Z+Xi/69NPC7VFie1jO8yxLeaU2BaNDH1J0sKaOKYvSToNQ1+SCmLoS1JBDH1JKoihL0kFMfQlqSD/H8RoW8eUwQZwAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import time\n",
    "#如果求解最大值必须将函数乘以-1，之后令sovmax=True\n",
    "\n",
    "def fun1s(X):#minfx\n",
    "    z=0.6224*X[:,0]*X[:,2]*X[:,3]+1.7781*X[:,1]*(X[:,2]**2)+3.1661*(X[:,0]**2)*X[:,3]+19.84*(X[:,0]**2)*X[:,2]\n",
    "    return z\n",
    "def aeqf(X):#不满足约束统一计算，返回的数组是0或满足\n",
    "    eq1=-1*X[:,0]+0.0193*X[:,2]\n",
    "    eq2=-1*X[:,1]+0.00954*X[:,2]\n",
    "    eq3=-1*np.math.pi*(X[:,2]**2)-4*np.math.pi*(X[:,2]**3)/3+129600\n",
    "    eq4=X[:,3]-240\n",
    "    result=np.zeros(X.shape[0])\n",
    "    for iter in range(X.shape[0]):#如果满足约束,此时所有变量应小于等于0\n",
    "        #judge\n",
    "        if  eq1[iter]<=0 and eq2[iter]<=0 and eq3[iter]<=0 and eq4[iter]<=0:\n",
    "            result[iter]=np.zeros(1)#满足条件，对应位置=0\n",
    "        else:#如果满足约束\n",
    "            result[iter]=np.ones(1)\n",
    "    \n",
    "    return result\n",
    "dim=4\n",
    "upper=[100,100,100,100]\n",
    "lower=[0,0,10,10]\n",
    "isints=[0,0,0,0]\n",
    "psize=500\n",
    "iter_num=1000\n",
    "pso=consadawpsoaeq(fun1s,lower,upper,dim,psize,isints,aeqf,ifplot=True,sovmax=False)\n",
    "starttime=time.time()\n",
    "pso.fit()\n",
    "endtime=time.time()\n",
    "print(f\"总耗时：{endtime-starttime}s\")\n",
    "pso.printresult()\n",
    "result=pso.getresult()#get x y\n",
    "resultx=result[0]\n",
    "#如果输出不带惩罚，那必然满足条件，带惩罚会输出一个负的（或者正的）巨大值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aeqf(resultx.reshape(1,resultx.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([863.72982303])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fun1s(resultx.reshape(1,resultx.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "总耗时：1.2475001811981201s\n",
      "最优变量:[6.43571429 0.57428571 0.        ]\n",
      "最优值是:14.594286\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import time\n",
    "#如果求解最大值必须将函数乘以-1，之后令sovmax=True\n",
    "def fun1s(X):\n",
    "    z=2*X[:,0]+3*X[:,1]-5*X[:,2]\n",
    "    return -z\n",
    "def aeqf(X):#不满足约束统一计算，返回的数组是0或满足\n",
    "    eq1=-1*(2*X[:,0]-5*X[:,1]+X[:,2]-10)\n",
    "    eq2=X[:,0]+3*X[:,1]+X[:,2]-12\n",
    "    result=np.zeros(X.shape[0])\n",
    "    for iter in range(X.shape[0]):#如果满足约束,此时所有变量应小于等于0\n",
    "        if  eq1[iter]<=0 and eq2[iter]<=0:\n",
    "            result[iter]=np.zeros(1)\n",
    "        else:#如果满足约束\n",
    "            result[iter]=np.ones(1)\n",
    "    return result\n",
    "def af(X):#有等式约束\n",
    "    eq1=X[:,0]+X[:,1]+X[:,2]-7\n",
    "    result=np.zeros(X.shape[0])\n",
    "    tol=0.01\n",
    "    #tol为容忍度，不建议太小\n",
    "    for iter in range(X.shape[0]):#如果满足约束,此时所有变量应等于0\n",
    "        if  np.abs(eq1[iter])<=tol:\n",
    "            result[iter]=np.zeros(1)\n",
    "        else:#如果满足约束\n",
    "            result[iter]=np.ones(1)\n",
    "    return result\n",
    "dim=3\n",
    "upper=[20,20,20]\n",
    "lower=[0,0,0]\n",
    "isints=[0,0,0]\n",
    "psize=600\n",
    "iter_num=1000\n",
    "pso=consadawpso(fun1s,lower,upper,dim,psize,isints,aeqf,af,max_v=3,ifplot=True,sovmax=True)\n",
    "#如果采用这种方法，max_v需要设置到较大，目前这个问题maxv=2-3较优\n",
    "#If there is an equality constraint, maxv has to be large\n",
    "starttime=time.time()\n",
    "pso.fit()\n",
    "endtime=time.time()\n",
    "print(f\"总耗时：{endtime-starttime}s\")\n",
    "pso.printresult()\n",
    "result=pso.getresult()#get x y\n",
    "#best x[]\n",
    "#y[]\n",
    "#可以看到能求出近似解来。\n",
    "#多运行几次求解，14.59结果"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.7 ('base')",
   "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": "3.9.7"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "16f5b46f222e2a3e8d4adbf7141cae37b71ed37616e60735fa5d1164a1bc3ada"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}