ACOA final.ipynb

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   "source": [
    "# Ant Colony Optimization Algorithm: Bin-Packing Problem"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e074eec0",
   "metadata": {},
   "source": [
    "Implementing the ACOA, which involves simulating artificial ants that iteratively place items into bins based on pheromone trails, adjusting the trails based on the quality of solutions found. Over time, ants converge toward better solutions, leading to the discovery of near-optimal packing configurations.\n",
    "The optimal solution in this case is defined as the smallest difference between the size of the bags, the goal is to place the items into the bins in such a way as to make the total weight in each bin as nearly equal as possible. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "35d13e79",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random as rn\n",
    "import numpy as np\n",
    "import time\n",
    "\n",
    "def ACO(bins, items, paths, evap_rate, fitness_evals, trials):\n",
    "    \n",
    "    '''\n",
    "    Ant colony optimization algorithm for finding the best fitness for the bin-packing problem.\n",
    "    Fitness is calculated as the difference between the largest and smallest bin.\n",
    "\n",
    "    Args:\n",
    "    \n",
    "    bins (int) : Number of bins\n",
    "    items (list(int)) : List of items\n",
    "    paths (int): Number of ant paths \n",
    "    evap_rate (float) : Rate of nodes evaporating\n",
    "    fitness_evals (int) : Number of fitness evaluations per trial\n",
    "    trials (int): Number of trials \n",
    "    \n",
    "    Returns:\n",
    "    \n",
    "    The best path's fitness found on the final evaluation across all trials.\n",
    "\n",
    "    '''\n",
    "    \n",
    "    # Record single best fitness acheived so far across trials\n",
    "    best_fitness_across_trials = float('inf')  # Initialize with positive infinity\n",
    "\n",
    "    for trial in range(trials):\n",
    "        \n",
    "        start_time = time.time()\n",
    "        \n",
    "        # Set a unique random seed for each trial\n",
    "        rn.seed(rn.randint(1, 10000))\n",
    "        \n",
    "        # Create random pheromones for each node\n",
    "        pheromone_paths = [[rn.uniform(0, 1) for i in range(len(items))] for x in range(bins)]\n",
    "\n",
    "        # Determining the amount of times to do the evaluations based on number of paths\n",
    "        runs = int(fitness_evals / paths)\n",
    "\n",
    "        for each_run in range(runs):\n",
    "        \n",
    "            fitness_list = []\n",
    "            path_list = []\n",
    "\n",
    "            for path in range(paths):\n",
    "            \n",
    "                path = navigate_path(items, pheromone_paths, bins)\n",
    "                path_list.append(path)\n",
    "            \n",
    "                # Get weights of bins\n",
    "                bin_list = [0 for y in range(bins)]\n",
    "\n",
    "                for i in range(len(path)):\n",
    "                    bin_list[path[i]] += items[i]\n",
    "\n",
    "                #Find the fitness of the individual path\n",
    "                fitness_list.append(max(bin_list) - min(bin_list))\n",
    "\n",
    "            # Update pheromones for  paths\n",
    "            current_path = 0\n",
    "            \n",
    "            for path in path_list:\n",
    "\n",
    "                current_path_node = 0\n",
    "\n",
    "                # Calculate the overall fitness of the path.\n",
    "                pheromone_update = (100 / fitness_list[current_path])\n",
    "\n",
    "                # Update pheromone path \n",
    "                for each_choice in path:\n",
    "\n",
    "                    pheromone_paths[each_choice][current_path_node] += pheromone_update\n",
    "                    current_path_node += 1\n",
    "\n",
    "                current_path += 1\n",
    "\n",
    "                each_run += 1\n",
    "        \n",
    "            # Evaporate nodes \n",
    "            pheromone_paths = evaporate(pheromone_paths, evap_rate)\n",
    "\n",
    "        # Update the best fitness across trials if a better fitness is found\n",
    "        if min(fitness_list) < best_fitness_across_trials:\n",
    "            best_fitness_across_trials = min(fitness_list)\n",
    "            \n",
    "        \n",
    "        end_time = time.time()  # Record the end time of the trial\n",
    "        trial_time = end_time - start_time  # Calculate the time taken for the trial\n",
    "        \n",
    "        print(f\"Trial {trial + 1} - Best fitness: {best_fitness_across_trials} - Time taken: {trial_time:.4f} seconds\")  # Print best fitness and trial time\n",
    "\n",
    "    print(bin_list)\n",
    "    return best_fitness_across_trials\n",
    "\n",
    "\n",
    "def navigate_path(items, pheromones, bins):\n",
    "    '''\n",
    "    Function for navigating paths based on pheromones. \n",
    "    \n",
    "    Args:\n",
    "    \n",
    "    items (list(int)): List of items in bins\n",
    "    pheromones (list(list(int))) : List of pheromones on paths\n",
    "    bins (int) : The number of bins\n",
    "\n",
    "    Returns:\n",
    "    \n",
    "    The path taken by the ant as it navigates its way through the bins\n",
    "\n",
    "    '''\n",
    "\n",
    "    path = []\n",
    "\n",
    "    # Iterate and place items in bins\n",
    "    for each_item in range(len(items)):\n",
    "\n",
    "        # Retrieving probability for the next bin\n",
    "        bin_prob = [bin_prob[each_item] for bin_prob in pheromones]\n",
    "\n",
    "        choice = rn.uniform(0, sum(bin_prob))\n",
    "        cumulative_prob = 0\n",
    "    \n",
    "        # Choose bin based on probability\n",
    "        for bin_prob_index in range(len(bin_prob)):\n",
    "            \n",
    "            cumulative_prob += bin_prob[bin_prob_index]\n",
    "\n",
    "            if choice <= cumulative_prob:\n",
    "                path.append(bin_prob_index)\n",
    "                break\n",
    "\n",
    "    return path\n",
    "\n",
    "\n",
    "\n",
    "def evaporate(pheromones, evap_rate):\n",
    "    '''\n",
    "    Evaporates all nodes in pheromone lists\n",
    "\n",
    "    Args:\n",
    "    \n",
    "    pheromones (list(list(int))) : Pheromone list to be updated\n",
    "    evap_rate (float) : Evaporation rate\n",
    "\n",
    "    Returns:\n",
    "    \n",
    "    The pheromones updated by the evaporation rate.\n",
    "    '''\n",
    "    return [[x * evap_rate for x in row] for row in pheromones]\n",
    "\n"
   ]
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   "cell_type": "code",
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   "id": "78fb822d",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BPP1: Experiment 1\n",
      "Trial 1 - Best fitness: 5856 - Time taken: 8.9806 seconds\n",
      "Trial 2 - Best fitness: 5856 - Time taken: 9.0845 seconds\n",
      "Trial 3 - Best fitness: 5856 - Time taken: 9.0665 seconds\n",
      "Trial 4 - Best fitness: 4708 - Time taken: 9.0888 seconds\n",
      "Trial 5 - Best fitness: 4708 - Time taken: 9.1614 seconds\n",
      "[34476, 22506, 30132, 27748, 20708, 25030, 23524, 20648, 22420, 22308]\n",
      "4708\n",
      "BPP1: Experiment 2\n",
      "Trial 1 - Best fitness: 5204 - Time taken: 9.1152 seconds\n",
      "Trial 2 - Best fitness: 5204 - Time taken: 9.1012 seconds\n",
      "Trial 3 - Best fitness: 4120 - Time taken: 9.0688 seconds\n",
      "Trial 4 - Best fitness: 4120 - Time taken: 9.1725 seconds\n",
      "Trial 5 - Best fitness: 4120 - Time taken: 9.1377 seconds\n",
      "[27960, 30226, 21894, 25818, 22564, 20528, 26934, 23750, 21986, 27840]\n",
      "4120\n",
      "BPP1: Experiment 3\n",
      "Trial 1 - Best fitness: 5782 - Time taken: 9.3347 seconds\n",
      "Trial 2 - Best fitness: 4642 - Time taken: 9.3144 seconds\n",
      "Trial 3 - Best fitness: 4642 - Time taken: 9.3728 seconds\n",
      "Trial 4 - Best fitness: 4188 - Time taken: 9.3601 seconds\n",
      "Trial 5 - Best fitness: 4188 - Time taken: 9.3460 seconds\n",
      "[26668, 28608, 24786, 21476, 28764, 22708, 26978, 23054, 21960, 24498]\n",
      "4188\n",
      "BPP1: Experiment 4\n",
      "Trial 1 - Best fitness: 888 - Time taken: 9.4138 seconds\n",
      "Trial 2 - Best fitness: 888 - Time taken: 9.3500 seconds\n",
      "Trial 3 - Best fitness: 888 - Time taken: 9.3811 seconds\n",
      "Trial 4 - Best fitness: 888 - Time taken: 9.4091 seconds\n",
      "Trial 5 - Best fitness: 888 - Time taken: 9.3579 seconds\n",
      "[25056, 25952, 25128, 24434, 24570, 24408, 25336, 24952, 24884, 24780]\n",
      "888\n"
     ]
    }
   ],
   "source": [
    "if __name__ == \"__main__\":\n",
    "    trials_per_experiment = 5  # Number of trials per experiment\n",
    "\n",
    "    print(\"BPP1: Experiment 1\")\n",
    "    print(ACO(10, [i * 2 for i in range(500)], 100, 0.90, 10000, trials_per_experiment))\n",
    "    \n",
    "    print(\"BPP1: Experiment 2\")\n",
    "    print(ACO(10, [i * 2 for i in range(500)], 100, 0.50, 10000, trials_per_experiment))\n",
    "    \n",
    "    print(\"BPP1: Experiment 3\")\n",
    "    print(ACO(10, [i * 2 for i in range(500)], 10, 0.90, 10000, trials_per_experiment))\n",
    "    \n",
    "    print(\"BPP1: Experiment 4\")\n",
    "    print(ACO(10, [i * 2 for i in range(500)], 10, 0.50, 10000, trials_per_experiment))"
   ]
  },
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   "cell_type": "code",
   "execution_count": 5,
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BPP2: Experiment 1\n",
      "Trial 1 - Best fitness: 9165 - Time taken: 20.6873 seconds\n",
      "Trial 2 - Best fitness: 8526 - Time taken: 20.9262 seconds\n",
      "Trial 3 - Best fitness: 7431 - Time taken: 20.8801 seconds\n",
      "Trial 4 - Best fitness: 7431 - Time taken: 21.1424 seconds\n",
      "Trial 5 - Best fitness: 7431 - Time taken: 20.9967 seconds\n",
      "[2661, 7398, 10563, 6588, 10209, 8664, 5856, 3132, 8088, 4983, 6153, 2829, 14679, 13179, 8427, 6294, 8250, 2619, 1254, 6291, 6333, 6849, 7932, 7731, 6618, 7947, 12690, 9684, 7773, 5964, 6150, 3945, 4413, 10572, 14712, 4092, 13455, 9096, 3462, 9552, 6723, 7260, 8874, 7389, 10062, 5043, 7515, 6390, 10536, 7371]\n",
      "7431\n",
      "BPP2: Experiment 2\n",
      "Trial 1 - Best fitness: 7497 - Time taken: 21.0300 seconds\n",
      "Trial 2 - Best fitness: 7497 - Time taken: 21.0669 seconds\n",
      "Trial 3 - Best fitness: 7497 - Time taken: 21.1303 seconds\n",
      "Trial 4 - Best fitness: 7497 - Time taken: 20.9916 seconds\n",
      "Trial 5 - Best fitness: 7497 - Time taken: 21.2235 seconds\n",
      "[6489, 10125, 7875, 6330, 4572, 7671, 5751, 6453, 10254, 6726, 6555, 7779, 10416, 6879, 7932, 7929, 6507, 9843, 8127, 3849, 8742, 4494, 6159, 7569, 10287, 4707, 5403, 2904, 7398, 9534, 7335, 7533, 10350, 8460, 10158, 3852, 10176, 4458, 9630, 4527, 4671, 7731, 6741, 7107, 6843, 14085, 8769, 5367, 9093, 12105]\n",
      "7497\n",
      "BPP2: Experiment 3\n",
      "Trial 1 - Best fitness: 6960 - Time taken: 22.1342 seconds\n",
      "Trial 2 - Best fitness: 6960 - Time taken: 22.0683 seconds\n",
      "Trial 3 - Best fitness: 6729 - Time taken: 22.3001 seconds\n",
      "Trial 4 - Best fitness: 6729 - Time taken: 22.2082 seconds\n",
      "Trial 5 - Best fitness: 6729 - Time taken: 22.3630 seconds\n",
      "[3507, 5355, 9087, 6471, 4071, 7641, 4428, 8274, 7317, 10440, 8334, 6342, 7446, 9960, 9357, 9375, 9114, 4251, 8268, 4629, 9480, 6636, 8808, 7401, 7164, 8655, 8262, 7701, 9993, 10353, 5082, 12207, 9129, 6828, 9279, 5076, 10827, 5649, 6417, 6840, 7377, 9288, 7485, 5364, 4935, 9477, 5448, 5139, 8040, 6243]\n",
      "6729\n",
      "BPP2: Experiment 4\n",
      "Trial 1 - Best fitness: 9075 - Time taken: 22.1752 seconds\n",
      "Trial 2 - Best fitness: 8109 - Time taken: 22.4055 seconds\n",
      "Trial 3 - Best fitness: 8109 - Time taken: 21.8667 seconds\n",
      "Trial 4 - Best fitness: 8109 - Time taken: 22.1576 seconds\n",
      "Trial 5 - Best fitness: 7203 - Time taken: 22.2871 seconds\n",
      "[9714, 9456, 9351, 5838, 10353, 5409, 5694, 4692, 9378, 6939, 10200, 4674, 7983, 9474, 7086, 5478, 5286, 8151, 11589, 5085, 9429, 5907, 11703, 8646, 7161, 7065, 5241, 5562, 4500, 8895, 4965, 7335, 8325, 5388, 9456, 8877, 6189, 7056, 9465, 10818, 6498, 8178, 5760, 4788, 8217, 5967, 9966, 6069, 8937, 6057]\n",
      "7203\n"
     ]
    }
   ],
   "source": [
    "if __name__ == \"__main__\":\n",
    "    trials_per_experiment = 5  # Number of trials per experiment\n",
    "    \n",
    "    print(\"BPP2: Experiment 1\")\n",
    "    print(ACO(50, [i * 3 for i in range(500)], 100, 0.90, 10000, trials_per_experiment))\n",
    "    \n",
    "    print(\"BPP2: Experiment 2\")\n",
    "    print(ACO(50, [i * 3 for i in range(500)], 100, 0.50, 10000, trials_per_experiment))\n",
    "    \n",
    "    print(\"BPP2: Experiment 3\")\n",
    "    print(ACO(50, [i * 3 for i in range(500)], 10, 0.90, 10000, trials_per_experiment))\n",
    "    \n",
    "    print(\"BPP2: Experiment 4\")\n",
    "    print(ACO(50, [i * 3 for i in range(500)], 10, 0.50, 10000, trials_per_experiment))"
   ]
  },
  {
   "cell_type": "code",
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   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def convergence_plot(experiment_results):\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    \n",
    "    for experiment_num, best_fitness_per_trial in enumerate(experiment_results, start=1):\n",
    "        plt.plot(range(1, len(best_fitness_per_trial) + 1), best_fitness_per_trial, marker='o', label=f'Experiment {experiment_num}')\n",
    "\n",
    "    plt.xlabel('Trial Number')\n",
    "    plt.ylabel('Best Fitness Value')\n",
    "    plt.title('BPP 1 Convergence Plot')\n",
    "    plt.grid(True)\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "# Example usage:\n",
    "experiment1_results = [5856,5856,5856,4708,4708]\n",
    "experiment2_results = [5204,5204,4120,4120,4120]\n",
    "experiment3_results = [5782,4642,4642,4188,4188]\n",
    "experiment4_results = [888,888,888,888,888]\n",
    "\n",
    "all_experiment_results = [experiment1_results, experiment2_results, experiment3_results, experiment4_results]\n",
    "\n",
    "convergence_plot(all_experiment_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d050ca4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def convergence_plot(experiment_results):\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    \n",
    "    for experiment_num, best_fitness_per_trial in enumerate(experiment_results, start=1):\n",
    "        plt.plot(range(1, len(best_fitness_per_trial) + 1), best_fitness_per_trial, marker='o', label=f'Experiment {experiment_num}')\n",
    "\n",
    "    plt.xlabel('Trial Number')\n",
    "    plt.ylabel('Best Fitness Value')\n",
    "    plt.title('BPP 2 Convergence Plot')\n",
    "    plt.grid(True)\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "# Example usage:\n",
    "experiment1_results = [9165,8526,7431,7431,7431]\n",
    "experiment2_results = [7497,7497,7497,7497,7497]\n",
    "experiment3_results = [6960,6960,6729,6729,6729]\n",
    "experiment4_results = [9075,8109,8109,8109,7203]\n",
    "\n",
    "all_experiment_results = [experiment1_results, experiment2_results, experiment3_results, experiment4_results]\n",
    "\n",
    "convergence_plot(all_experiment_results)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5b3452b",
   "metadata": {},
   "source": [
    "***Reference:***\n",
    "\n",
    "Some of the functions I used were inspired and adapted by a similar implementation of the ACO algorithm that can be found here:\n",
    "\n",
    "https://github.com/Enchando/Ant-Colony-Optimisation/tree/master"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}