ANN_ALO(mealpy)_airfoil_dataset.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4cd1c3db",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import modules\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler, QuantileTransformer, MaxAbsScaler\n",
    "from sklearn.metrics import r2_score, mean_absolute_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7b7066bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: mealpy in c:\\users\\nithi\\anaconda3\\lib\\site-packages (3.0.1)\n",
      "Requirement already satisfied: numpy>=1.17.5 in c:\\users\\nithi\\anaconda3\\lib\\site-packages (from mealpy) (1.24.3)\n",
      "Requirement already satisfied: matplotlib>=3.3.0 in c:\\users\\nithi\\anaconda3\\lib\\site-packages (from mealpy) (3.7.1)\n",
      "Requirement already satisfied: scipy>=1.7.1 in c:\\users\\nithi\\anaconda3\\lib\\site-packages (from mealpy) (1.10.1)\n",
      "Requirement already satisfied: pandas>=1.2.0 in c:\\users\\nithi\\anaconda3\\lib\\site-packages (from mealpy) (1.5.3)\n",
      "Requirement already satisfied: opfunu>=1.0.0 in c:\\users\\nithi\\anaconda3\\lib\\site-packages (from mealpy) (1.0.1)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\nithi\\anaconda3\\lib\\site-packages (from matplotlib>=3.3.0->mealpy) (1.0.5)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\nithi\\anaconda3\\lib\\site-packages (from matplotlib>=3.3.0->mealpy) (0.11.0)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\nithi\\anaconda3\\lib\\site-packages (from matplotlib>=3.3.0->mealpy) (4.25.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\nithi\\anaconda3\\lib\\site-packages (from matplotlib>=3.3.0->mealpy) (1.4.4)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\nithi\\anaconda3\\lib\\site-packages (from matplotlib>=3.3.0->mealpy) (23.0)\n",
      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\nithi\\anaconda3\\lib\\site-packages (from matplotlib>=3.3.0->mealpy) (9.4.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\nithi\\anaconda3\\lib\\site-packages (from matplotlib>=3.3.0->mealpy) (3.0.9)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\nithi\\anaconda3\\lib\\site-packages (from matplotlib>=3.3.0->mealpy) (2.8.2)\n",
      "Requirement already satisfied: requests>=2.27.0 in c:\\users\\nithi\\anaconda3\\lib\\site-packages (from opfunu>=1.0.0->mealpy) (2.31.0)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\nithi\\anaconda3\\lib\\site-packages (from pandas>=1.2.0->mealpy) (2022.7)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\nithi\\anaconda3\\lib\\site-packages (from python-dateutil>=2.7->matplotlib>=3.3.0->mealpy) (1.16.0)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\users\\nithi\\anaconda3\\lib\\site-packages (from requests>=2.27.0->opfunu>=1.0.0->mealpy) (2.0.4)\n",
      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\nithi\\anaconda3\\lib\\site-packages (from requests>=2.27.0->opfunu>=1.0.0->mealpy) (3.4)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\users\\nithi\\anaconda3\\lib\\site-packages (from requests>=2.27.0->opfunu>=1.0.0->mealpy) (1.26.16)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\nithi\\anaconda3\\lib\\site-packages (from requests>=2.27.0->opfunu>=1.0.0->mealpy) (2023.7.22)\n"
     ]
    }
   ],
   "source": [
    "!pip install mealpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "71ceeff6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Input, Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "114db60b",
   "metadata": {},
   "outputs": [],
   "source": [
    "url_file = 'https://archive.ics.uci.edu/ml/machine-learning-databases/00291/airfoil_self_noise.dat'\n",
    "columns = ['frequency',\n",
    "           'angle_of_attack', \n",
    "           'chord_length', \n",
    "           'free_stream_velocity',\n",
    "           'suction_side_displacement_thickness',\n",
    "           'scaled_sound_pressure_level']\n",
    "\n",
    "features = ['frequency','angle_of_attack', \n",
    "            'chord_length', \n",
    "            'free_stream_velocity',\n",
    "            'suction_side_displacement_thickness']\n",
    "\n",
    "df = pd.read_csv(url_file, sep='\\t', header=None, names=columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0c6abd23",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>frequency</th>\n",
       "      <th>angle_of_attack</th>\n",
       "      <th>chord_length</th>\n",
       "      <th>free_stream_velocity</th>\n",
       "      <th>suction_side_displacement_thickness</th>\n",
       "      <th>scaled_sound_pressure_level</th>\n",
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       "      <th>0</th>\n",
       "      <td>800</td>\n",
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       "      <td>0.3048</td>\n",
       "      <td>71.3</td>\n",
       "      <td>0.002663</td>\n",
       "      <td>126.201</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3048</td>\n",
       "      <td>71.3</td>\n",
       "      <td>0.002663</td>\n",
       "      <td>125.201</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3048</td>\n",
       "      <td>71.3</td>\n",
       "      <td>0.002663</td>\n",
       "      <td>125.951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1600</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3048</td>\n",
       "      <td>71.3</td>\n",
       "      <td>0.002663</td>\n",
       "      <td>127.591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3048</td>\n",
       "      <td>71.3</td>\n",
       "      <td>0.002663</td>\n",
       "      <td>127.461</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   frequency  angle_of_attack  chord_length  free_stream_velocity  \\\n",
       "0        800              0.0        0.3048                  71.3   \n",
       "1       1000              0.0        0.3048                  71.3   \n",
       "2       1250              0.0        0.3048                  71.3   \n",
       "3       1600              0.0        0.3048                  71.3   \n",
       "4       2000              0.0        0.3048                  71.3   \n",
       "\n",
       "   suction_side_displacement_thickness  scaled_sound_pressure_level  \n",
       "0                             0.002663                      126.201  \n",
       "1                             0.002663                      125.201  \n",
       "2                             0.002663                      125.951  \n",
       "3                             0.002663                      127.591  \n",
       "4                             0.002663                      127.461  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b927a7dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>frequency</th>\n",
       "      <th>angle_of_attack</th>\n",
       "      <th>chord_length</th>\n",
       "      <th>free_stream_velocity</th>\n",
       "      <th>suction_side_displacement_thickness</th>\n",
       "      <th>scaled_sound_pressure_level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1498</th>\n",
       "      <td>2500</td>\n",
       "      <td>15.6</td>\n",
       "      <td>0.1016</td>\n",
       "      <td>39.6</td>\n",
       "      <td>0.052849</td>\n",
       "      <td>110.264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1499</th>\n",
       "      <td>3150</td>\n",
       "      <td>15.6</td>\n",
       "      <td>0.1016</td>\n",
       "      <td>39.6</td>\n",
       "      <td>0.052849</td>\n",
       "      <td>109.254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1500</th>\n",
       "      <td>4000</td>\n",
       "      <td>15.6</td>\n",
       "      <td>0.1016</td>\n",
       "      <td>39.6</td>\n",
       "      <td>0.052849</td>\n",
       "      <td>106.604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1501</th>\n",
       "      <td>5000</td>\n",
       "      <td>15.6</td>\n",
       "      <td>0.1016</td>\n",
       "      <td>39.6</td>\n",
       "      <td>0.052849</td>\n",
       "      <td>106.224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1502</th>\n",
       "      <td>6300</td>\n",
       "      <td>15.6</td>\n",
       "      <td>0.1016</td>\n",
       "      <td>39.6</td>\n",
       "      <td>0.052849</td>\n",
       "      <td>104.204</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      frequency  angle_of_attack  chord_length  free_stream_velocity  \\\n",
       "1498       2500             15.6        0.1016                  39.6   \n",
       "1499       3150             15.6        0.1016                  39.6   \n",
       "1500       4000             15.6        0.1016                  39.6   \n",
       "1501       5000             15.6        0.1016                  39.6   \n",
       "1502       6300             15.6        0.1016                  39.6   \n",
       "\n",
       "      suction_side_displacement_thickness  scaled_sound_pressure_level  \n",
       "1498                             0.052849                      110.264  \n",
       "1499                             0.052849                      109.254  \n",
       "1500                             0.052849                      106.604  \n",
       "1501                             0.052849                      106.224  \n",
       "1502                             0.052849                      104.204  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ba40ec39",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>frequency</th>\n",
       "      <th>angle_of_attack</th>\n",
       "      <th>chord_length</th>\n",
       "      <th>free_stream_velocity</th>\n",
       "      <th>suction_side_displacement_thickness</th>\n",
       "      <th>scaled_sound_pressure_level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1503.000000</td>\n",
       "      <td>1503.000000</td>\n",
       "      <td>1503.000000</td>\n",
       "      <td>1503.000000</td>\n",
       "      <td>1503.000000</td>\n",
       "      <td>1503.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2886.380572</td>\n",
       "      <td>6.782302</td>\n",
       "      <td>0.136548</td>\n",
       "      <td>50.860745</td>\n",
       "      <td>0.011140</td>\n",
       "      <td>124.835943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3152.573137</td>\n",
       "      <td>5.918128</td>\n",
       "      <td>0.093541</td>\n",
       "      <td>15.572784</td>\n",
       "      <td>0.013150</td>\n",
       "      <td>6.898657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>200.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.025400</td>\n",
       "      <td>31.700000</td>\n",
       "      <td>0.000401</td>\n",
       "      <td>103.380000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>800.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.050800</td>\n",
       "      <td>39.600000</td>\n",
       "      <td>0.002535</td>\n",
       "      <td>120.191000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1600.000000</td>\n",
       "      <td>5.400000</td>\n",
       "      <td>0.101600</td>\n",
       "      <td>39.600000</td>\n",
       "      <td>0.004957</td>\n",
       "      <td>125.721000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4000.000000</td>\n",
       "      <td>9.900000</td>\n",
       "      <td>0.228600</td>\n",
       "      <td>71.300000</td>\n",
       "      <td>0.015576</td>\n",
       "      <td>129.995500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>20000.000000</td>\n",
       "      <td>22.200000</td>\n",
       "      <td>0.304800</td>\n",
       "      <td>71.300000</td>\n",
       "      <td>0.058411</td>\n",
       "      <td>140.987000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          frequency  angle_of_attack  chord_length  free_stream_velocity  \\\n",
       "count   1503.000000      1503.000000   1503.000000           1503.000000   \n",
       "mean    2886.380572         6.782302      0.136548             50.860745   \n",
       "std     3152.573137         5.918128      0.093541             15.572784   \n",
       "min      200.000000         0.000000      0.025400             31.700000   \n",
       "25%      800.000000         2.000000      0.050800             39.600000   \n",
       "50%     1600.000000         5.400000      0.101600             39.600000   \n",
       "75%     4000.000000         9.900000      0.228600             71.300000   \n",
       "max    20000.000000        22.200000      0.304800             71.300000   \n",
       "\n",
       "       suction_side_displacement_thickness  scaled_sound_pressure_level  \n",
       "count                          1503.000000                  1503.000000  \n",
       "mean                              0.011140                   124.835943  \n",
       "std                               0.013150                     6.898657  \n",
       "min                               0.000401                   103.380000  \n",
       "25%                               0.002535                   120.191000  \n",
       "50%                               0.004957                   125.721000  \n",
       "75%                               0.015576                   129.995500  \n",
       "max                               0.058411                   140.987000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cfc7d504",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1503 entries, 0 to 1502\n",
      "Data columns (total 6 columns):\n",
      " #   Column                               Non-Null Count  Dtype  \n",
      "---  ------                               --------------  -----  \n",
      " 0   frequency                            1503 non-null   int64  \n",
      " 1   angle_of_attack                      1503 non-null   float64\n",
      " 2   chord_length                         1503 non-null   float64\n",
      " 3   free_stream_velocity                 1503 non-null   float64\n",
      " 4   suction_side_displacement_thickness  1503 non-null   float64\n",
      " 5   scaled_sound_pressure_level          1503 non-null   float64\n",
      "dtypes: float64(5), int64(1)\n",
      "memory usage: 70.6 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d5f5320f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_input(df):\n",
    "    \n",
    "    train_data = df.copy()\n",
    "    train_set = train_data.drop(\"scaled_sound_pressure_level\", axis = 1)\n",
    "    train_labels = train_data[\"scaled_sound_pressure_level\"].copy()\n",
    "    \n",
    "    train_set = QuantileTransformer(random_state=0).fit_transform(train_set)\n",
    "    train_set = MaxAbsScaler().fit_transform(train_set)\n",
    "    train_set = StandardScaler().fit_transform(train_set)\n",
    "    \n",
    "    # Train-test split\n",
    "    X_train, X_test, y_train, y_test = train_test_split(train_set, train_labels, test_size=0.2)\n",
    "    \n",
    "    return X_train, X_test, y_train, y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b577ebf7",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = preprocess_input(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d486047c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1202, 5)\n",
      "(301, 5)\n",
      "(1202,)\n",
      "(301,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5b3219e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense (Dense)               (None, 32)                192       \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 1)                 33        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 225 (900.00 Byte)\n",
      "Trainable params: 225 (900.00 Byte)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(Input(X_train.shape[1],))\n",
    "\n",
    "model.add(Dense(32, activation = 'relu'))\n",
    "\n",
    "model.add(Dense(1))\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5f0f3766",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss = 'mean_squared_error', metrics = tf.keras.metrics.RootMeanSquaredError())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "38595e5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_shape(model):\n",
    "    weights_layer = model.get_weights()\n",
    "    shapes = []\n",
    "    for weights in weights_layer:\n",
    "        shapes.append(weights.shape)\n",
    "    return shapes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7c976809",
   "metadata": {},
   "outputs": [],
   "source": [
    "def set_shape(weights,shapes):\n",
    "    new_weights = []\n",
    "    index=0\n",
    "    for shape in shapes:\n",
    "        if(len(shape)>1):\n",
    "            n_nodes = np.prod(shape) + index\n",
    "        else:\n",
    "            n_nodes=shape[0]+index\n",
    "        tmp = np.array(weights[index:n_nodes]).reshape(shape)\n",
    "        new_weights.append(tmp)\n",
    "        index=n_nodes\n",
    "    return new_weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "de82e63a",
   "metadata": {},
   "outputs": [],
   "source": [
    "shape = get_shape(model)\n",
    "num_solutions = 15\n",
    "dimensions = 225"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0d660063",
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_nn(W):\n",
    "    model.set_weights(set_shape(W,shape))\n",
    "    score = model.evaluate(X_train, y_train, verbose = 0)\n",
    "    return score[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e51d85f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from mealpy import ALO, FloatVar\n",
    "lb = [-10 for i in range(dimensions)]\n",
    "ub = [10 for i in range(dimensions)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4ff95e38",
   "metadata": {},
   "outputs": [],
   "source": [
    "problem_dict = {\n",
    "    \"bounds\": FloatVar(lb = lb, ub = ub, name=\"NeuralNetwork\"),\n",
    "    \"minmax\": \"min\",\n",
    "    \"obj_func\": evaluate_nn\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d13a5e69",
   "metadata": {},
   "outputs": [],
   "source": [
    "ALO_optimizer = ALO.OriginalALO(epoch=1000, pop_size=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3f747770",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024/02/28 01:29:28 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: Solving single objective optimization problem.\n",
      "2024/02/28 01:29:35 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 1, Current best: 93.03325653076172, Global best: 93.03325653076172, Runtime: 3.25692 seconds\n",
      "2024/02/28 01:29:38 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 2, Current best: 93.03325653076172, Global best: 93.03325653076172, Runtime: 3.50131 seconds\n",
      "2024/02/28 01:29:41 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 3, Current best: 93.03325653076172, Global best: 93.03325653076172, Runtime: 3.37244 seconds\n",
      "2024/02/28 01:29:45 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 4, Current best: 93.03325653076172, Global best: 93.03325653076172, Runtime: 3.38634 seconds\n",
      "2024/02/28 01:29:48 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 5, Current best: 43.59540557861328, Global best: 43.59540557861328, Runtime: 3.23725 seconds\n",
      "2024/02/28 01:29:51 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 6, Current best: 43.59540557861328, Global best: 43.59540557861328, Runtime: 3.23899 seconds\n",
      "2024/02/28 01:29:54 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 7, Current best: 43.59540557861328, Global best: 43.59540557861328, Runtime: 3.00983 seconds\n",
      "2024/02/28 01:29:57 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 8, Current best: 43.59540557861328, Global best: 43.59540557861328, Runtime: 3.02135 seconds\n",
      "2024/02/28 01:30:00 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 9, Current best: 43.59540557861328, Global best: 43.59540557861328, Runtime: 3.10076 seconds\n",
      "2024/02/28 01:30:03 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 10, Current best: 43.59540557861328, Global best: 43.59540557861328, Runtime: 2.98424 seconds\n",
      "2024/02/28 01:30:06 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 11, Current best: 43.45619201660156, Global best: 43.45619201660156, Runtime: 2.97808 seconds\n",
      "2024/02/28 01:30:09 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 12, Current best: 43.30631637573242, Global best: 43.30631637573242, Runtime: 2.77226 seconds\n",
      "2024/02/28 01:30:12 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 13, Current best: 43.30631637573242, Global best: 43.30631637573242, Runtime: 2.73091 seconds\n",
      "2024/02/28 01:30:14 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 14, Current best: 43.30631637573242, Global best: 43.30631637573242, Runtime: 2.39256 seconds\n",
      "2024/02/28 01:30:17 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 15, Current best: 43.3051643371582, Global best: 43.3051643371582, Runtime: 2.45970 seconds\n",
      "2024/02/28 01:30:20 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 16, Current best: 43.3051643371582, Global best: 43.3051643371582, Runtime: 2.92069 seconds\n",
      "2024/02/28 01:30:23 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 17, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 3.55853 seconds\n",
      "2024/02/28 01:30:27 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 18, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 3.29981 seconds\n",
      "2024/02/28 01:30:30 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 19, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 3.18755 seconds\n",
      "2024/02/28 01:30:33 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 20, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 3.28465 seconds\n",
      "2024/02/28 01:30:36 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 21, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 3.20582 seconds\n",
      "2024/02/28 01:30:40 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 22, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 3.57334 seconds\n",
      "2024/02/28 01:30:43 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 23, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 3.37420 seconds\n",
      "2024/02/28 01:30:46 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 24, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 3.19448 seconds\n",
      "2024/02/28 01:30:50 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 25, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 3.29895 seconds\n",
      "2024/02/28 01:30:53 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 26, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 3.16211 seconds\n",
      "2024/02/28 01:30:56 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 27, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 3.33285 seconds\n",
      "2024/02/28 01:30:59 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 28, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 3.09428 seconds\n",
      "2024/02/28 01:31:02 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 29, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 3.18974 seconds\n",
      "2024/02/28 01:31:06 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 30, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 3.12526 seconds\n",
      "2024/02/28 01:31:09 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 31, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 3.47216 seconds\n",
      "2024/02/28 01:31:12 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 32, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 3.28632 seconds\n",
      "2024/02/28 01:31:15 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 33, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 3.06517 seconds\n",
      "2024/02/28 01:31:19 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 34, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 3.21384 seconds\n",
      "2024/02/28 01:31:22 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 35, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 3.49458 seconds\n",
      "2024/02/28 01:31:26 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 36, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 3.34634 seconds\n",
      "2024/02/28 01:31:30 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 37, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 4.01469 seconds\n",
      "2024/02/28 01:31:36 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 38, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 6.63221 seconds\n",
      "2024/02/28 01:31:41 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 39, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 4.75276 seconds\n",
      "2024/02/28 01:31:44 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 40, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 3.52033 seconds\n",
      "2024/02/28 01:31:47 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 41, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 2.94443 seconds\n",
      "2024/02/28 01:31:51 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 42, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 3.33423 seconds\n",
      "2024/02/28 01:31:54 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 43, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 3.50108 seconds\n",
      "2024/02/28 01:31:57 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 44, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 3.09146 seconds\n",
      "2024/02/28 01:32:02 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 45, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 4.35853 seconds\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024/02/28 01:32:05 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 46, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 3.13472 seconds\n",
      "2024/02/28 01:32:07 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 47, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 2.53602 seconds\n",
      "2024/02/28 01:32:10 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 48, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 2.54202 seconds\n",
      "2024/02/28 01:32:13 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 49, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 3.53342 seconds\n",
      "2024/02/28 01:32:17 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 50, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 3.20964 seconds\n",
      "2024/02/28 01:32:19 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 51, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 2.61695 seconds\n",
      "2024/02/28 01:32:23 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 52, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 3.70016 seconds\n",
      "2024/02/28 01:32:29 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 53, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 6.16055 seconds\n",
      "2024/02/28 01:32:36 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 54, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 6.81339 seconds\n",
      "2024/02/28 01:32:40 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 55, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 4.18381 seconds\n",
      "2024/02/28 01:32:44 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 56, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 3.44190 seconds\n",
      "2024/02/28 01:32:46 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 57, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 2.91539 seconds\n",
      "2024/02/28 01:32:50 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 58, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 3.21150 seconds\n",
      "2024/02/28 01:32:53 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 59, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 3.12996 seconds\n",
      "2024/02/28 01:32:56 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 60, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 3.29410 seconds\n",
      "2024/02/28 01:33:00 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 61, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 3.55905 seconds\n",
      "2024/02/28 01:33:04 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 62, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 4.15141 seconds\n",
      "2024/02/28 01:33:07 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 63, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 3.56502 seconds\n",
      "2024/02/28 01:33:11 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 64, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 3.52268 seconds\n",
      "2024/02/28 01:33:15 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 65, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 3.66312 seconds\n",
      "2024/02/28 01:33:18 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 66, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 3.19639 seconds\n",
      "2024/02/28 01:33:21 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 67, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 2.87399 seconds\n",
      "2024/02/28 01:33:24 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 68, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 2.96577 seconds\n",
      "2024/02/28 01:33:26 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 69, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 2.72490 seconds\n",
      "2024/02/28 01:33:29 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 70, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 2.61784 seconds\n",
      "2024/02/28 01:33:32 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 71, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 2.76055 seconds\n",
      "2024/02/28 01:33:35 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 72, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 2.78770 seconds\n",
      "2024/02/28 01:33:38 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 73, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 3.02091 seconds\n",
      "2024/02/28 01:33:40 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 74, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 2.84260 seconds\n",
      "2024/02/28 01:33:44 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 75, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 3.12271 seconds\n",
      "2024/02/28 01:33:47 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 76, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 3.27721 seconds\n",
      "2024/02/28 01:33:50 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 77, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 3.47386 seconds\n",
      "2024/02/28 01:33:54 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 78, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 3.70146 seconds\n",
      "2024/02/28 01:33:58 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 79, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 3.78444 seconds\n",
      "2024/02/28 01:34:02 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 80, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 3.94836 seconds\n",
      "2024/02/28 01:34:05 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 81, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 3.63360 seconds\n",
      "2024/02/28 01:34:09 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 82, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 3.57461 seconds\n",
      "2024/02/28 01:34:13 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 83, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 3.56128 seconds\n",
      "2024/02/28 01:34:17 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 84, Current best: 43.304908752441406, Global best: 43.304908752441406, Runtime: 4.41318 seconds\n",
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      "2024/02/28 02:24:45 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 983, Current best: 8.700098991394043, Global best: 8.700098991394043, Runtime: 3.13360 seconds\n",
      "2024/02/28 02:24:47 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 984, Current best: 8.700092315673828, Global best: 8.700092315673828, Runtime: 2.53710 seconds\n",
      "2024/02/28 02:24:49 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 985, Current best: 8.700091361999512, Global best: 8.700091361999512, Runtime: 1.98954 seconds\n",
      "2024/02/28 02:24:52 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 986, Current best: 8.700085639953613, Global best: 8.700085639953613, Runtime: 2.19744 seconds\n",
      "2024/02/28 02:24:54 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 987, Current best: 8.700080871582031, Global best: 8.700080871582031, Runtime: 2.45459 seconds\n",
      "2024/02/28 02:24:56 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 988, Current best: 8.70007610321045, Global best: 8.70007610321045, Runtime: 2.39973 seconds\n",
      "2024/02/28 02:24:59 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 989, Current best: 8.700068473815918, Global best: 8.700068473815918, Runtime: 2.16375 seconds\n",
      "2024/02/28 02:25:01 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 990, Current best: 8.700067520141602, Global best: 8.700067520141602, Runtime: 1.97910 seconds\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024/02/28 02:25:03 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 991, Current best: 8.700063705444336, Global best: 8.700063705444336, Runtime: 2.43604 seconds\n",
      "2024/02/28 02:25:06 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 992, Current best: 8.700053215026855, Global best: 8.700053215026855, Runtime: 2.58631 seconds\n",
      "2024/02/28 02:25:08 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 993, Current best: 8.700045585632324, Global best: 8.700045585632324, Runtime: 2.53591 seconds\n",
      "2024/02/28 02:25:10 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 994, Current best: 8.700044631958008, Global best: 8.700044631958008, Runtime: 2.30840 seconds\n",
      "2024/02/28 02:25:13 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 995, Current best: 8.70003604888916, Global best: 8.70003604888916, Runtime: 2.23924 seconds\n",
      "2024/02/28 02:25:15 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 996, Current best: 8.700035095214844, Global best: 8.700035095214844, Runtime: 2.46711 seconds\n",
      "2024/02/28 02:25:18 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 997, Current best: 8.700032234191895, Global best: 8.700032234191895, Runtime: 2.82986 seconds\n",
      "2024/02/28 02:25:21 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 998, Current best: 8.700021743774414, Global best: 8.700021743774414, Runtime: 2.68824 seconds\n",
      "2024/02/28 02:25:23 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 999, Current best: 8.700014114379883, Global best: 8.700014114379883, Runtime: 2.75391 seconds\n",
      "2024/02/28 02:25:26 PM, INFO, mealpy.swarm_based.ALO.OriginalALO: >>>Problem: P, Epoch: 1000, Current best: 8.7000093460083, Global best: 8.7000093460083, Runtime: 2.84321 seconds\n"
     ]
    }
   ],
   "source": [
    "g_best = ALO_optimizer.solve(problem_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "74579590",
   "metadata": {},
   "outputs": [],
   "source": [
    "solution = g_best.solution\n",
    "model.set_weights(set_shape(solution, shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c80edfc4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "38/38 [==============================] - 0s 2ms/step - loss: 75.6902 - root_mean_squared_error: 8.7000\n",
      "10/10 [==============================] - 0s 2ms/step - loss: 80.5227 - root_mean_squared_error: 8.9734\n",
      "Train RMSE =  8.7000093460083\n",
      "Test RMSE =  8.973445892333984\n",
      "---------------------------\n",
      "Train MSE =  75.69015502929688\n",
      "Test MSE =  80.52273559570312\n",
      "---------------------------\n",
      "38/38 [==============================] - 0s 1ms/step\n",
      "10/10 [==============================] - 0s 1ms/step\n",
      "Train R2 =  -0.6083204643680564\n",
      "Test R2 =  -0.6252191885401774\n",
      "-----------------------------\n",
      "Train MAE =  6.830878044902783\n",
      "Test MAE =  6.928432395352082\n"
     ]
    }
   ],
   "source": [
    "# RMSE\n",
    "score = model.evaluate(X_train, y_train)\n",
    "train_rmse, train_mse = score[1], score[0]\n",
    "\n",
    "score = model.evaluate(X_test, y_test)\n",
    "test_rmse, test_mse = score[1], score[0]\n",
    "\n",
    "print(\"Train RMSE = \", train_rmse)\n",
    "print(\"Test RMSE = \", test_rmse)\n",
    "print(\"---------------------------\")\n",
    "\n",
    "#MSE\n",
    "print(\"Train MSE = \", train_mse)\n",
    "print(\"Test MSE = \", test_mse)\n",
    "print(\"---------------------------\")\n",
    "\n",
    "#R2 Score\n",
    "y_train_pred = model.predict(X_train)\n",
    "y_test_pred = model.predict(X_test)\n",
    "train_r2_score = r2_score(y_train, y_train_pred)\n",
    "test_r2_score = r2_score(y_test, y_test_pred)\n",
    "\n",
    "print(\"Train R2 = \", train_r2_score)\n",
    "print(\"Test R2 = \", test_r2_score)\n",
    "print(\"-----------------------------\")\n",
    "\n",
    "# MAE\n",
    "train_mae = mean_absolute_error(y_train, y_train_pred)\n",
    "test_mae = mean_absolute_error(y_test, y_test_pred)\n",
    "print(\"Train MAE = \", train_mae)\n",
    "print(\"Test MAE = \", test_mae)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a04d477",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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