Ant_Colony_Optimization_7.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "***OTHER APPLICATIONS OF ANT COLONY OPTIMIZATION***\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "V1qE1KIfyvft"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**`Connection Oriented Network Routing`**"
      ],
      "metadata": {
        "id": "XIjT7s5Qy2Yu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import networkx as nx\n",
        "import random\n",
        "import numpy as np\n",
        "G = nx.DiGraph()\n",
        "G.add_nodes_from([1, 2, 3, 4, 5])\n",
        "G.add_edges_from([(1, 2, {'cost': 2, 'pheromone': 1, 'bandwidth': 10}),\n",
        "                   (1, 3, {'cost': 3, 'pheromone': 1, 'bandwidth': 8}),\n",
        "                   (2, 4, {'cost': 1, 'pheromone': 1, 'bandwidth': 5}),\n",
        "                   (3, 4, {'cost': 4, 'pheromone': 1, 'bandwidth': 6}),\n",
        "                   (4, 5, {'cost': 2, 'pheromone': 1, 'bandwidth': 7}),\n",
        "                   (3, 5, {'cost': 3, 'pheromone': 1, 'bandwidth': 9})])\n",
        "class AntColonyOptimization:\n",
        "    def __init__(self, graph, colony_size, alpha=1.0, beta=2.0, evaporation_rate=0.5):\n",
        "        self.graph = graph\n",
        "        self.colony_size = colony_size\n",
        "        self.alpha = alpha\n",
        "        self.beta = beta\n",
        "        self.evaporation_rate = evaporation_rate\n",
        "        self.pheromone_matrix = self.initialize_pheromones()\n",
        "    def initialize_pheromones(self):\n",
        "        pheromones = nx.get_edge_attributes(self.graph, 'pheromone')\n",
        "        return {edge: pheromones.get(edge, 1.0) for edge in self.graph.edges()}\n",
        "    def select_path(self, ant, available_paths):\n",
        "        probabilities = [\n",
        "            ((self.pheromone_matrix[edge] ** self.alpha) * (1.0 / self.graph[edge[0]][edge[1]]['cost']) ** self.beta) for\n",
        "            edge in available_paths]\n",
        "        probabilities /= np.sum(probabilities)\n",
        "        selected_path = random.choices(available_paths, probabilities)[0]\n",
        "        return selected_path\n",
        "    def update_pheromones(self, ant_paths):\n",
        "        self.pheromone_matrix = {edge: (1.0 - self.evaporation_rate) * pheromone for edge, pheromone in\n",
        "                                 self.pheromone_matrix.items()}\n",
        "        for path in ant_paths:\n",
        "            total_cost = sum([1.0 / self.graph[edge[0]][edge[1]]['bandwidth'] for edge in path])\n",
        "            pheromone_deposit = 1.0 / total_cost\n",
        "            for i in range(len(path) - 1):\n",
        "                edge = path[i]\n",
        "                self.pheromone_matrix[edge] += pheromone_deposit\n",
        "    def find_path(self, source, destination):\n",
        "        ant_paths = []\n",
        "        for _ in range(self.colony_size):\n",
        "            current_node = source\n",
        "            ant_path = []\n",
        "            while current_node != destination:\n",
        "                available_paths = [\n",
        "                    (current_node, neighbor) for neighbor in self.graph.successors(current_node) if\n",
        "                    (current_node, neighbor) not in ant_path]\n",
        "                if not available_paths:\n",
        "                    break\n",
        "                selected_path = self.select_path(_, available_paths)\n",
        "                ant_path.append(selected_path)\n",
        "                current_node = selected_path[1]\n",
        "            ant_paths.append(ant_path)\n",
        "        self.update_pheromones(ant_paths)\n",
        "        best_path = max(ant_paths, key=lambda path: sum([1.0 / self.graph[edge[0]][edge[1]]['bandwidth'] for edge in path]))\n",
        "        return best_path\n",
        "aco = AntColonyOptimization(G, colony_size=5, alpha=1.0, beta=2.0, evaporation_rate=0.5)\n",
        "source_node = 1\n",
        "destination_node = 5\n",
        "best_route = aco.find_path(source_node, destination_node)\n",
        "print(\"Best Route =\", best_route)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mMp1OmoO7XQc",
        "outputId": "0980cf2c-5d5e-414d-ec62-ffe2e7022f53"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best Route = [(1, 2), (2, 4), (4, 5)]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import networkx as nx\n",
        "import random\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "G = nx.DiGraph()\n",
        "G.add_nodes_from([1, 2, 3, 4, 5])\n",
        "G.add_edges_from([(1, 2, {'cost': 2, 'pheromone': 1, 'bandwidth': 10}),\n",
        "                   (1, 3, {'cost': 3, 'pheromone': 1, 'bandwidth': 8}),\n",
        "                   (2, 4, {'cost': 1, 'pheromone': 1, 'bandwidth': 5}),\n",
        "                   (3, 4, {'cost': 4, 'pheromone': 1, 'bandwidth': 6}),\n",
        "                   (4, 5, {'cost': 2, 'pheromone': 1, 'bandwidth': 7}),\n",
        "                   (3, 5, {'cost': 3, 'pheromone': 1, 'bandwidth': 9})])\n",
        "class AntColonyOptimization:\n",
        "    def __init__(self, graph, colony_size, alpha=1.0, beta=2.0, evaporation_rate=0.5):\n",
        "        self.graph = graph\n",
        "        self.colony_size = colony_size\n",
        "        self.alpha = alpha\n",
        "        self.beta = beta\n",
        "        self.evaporation_rate = evaporation_rate\n",
        "        self.pheromone_matrix = self.initialize_pheromones()\n",
        "    def initialize_pheromones(self):\n",
        "        pheromones = nx.get_edge_attributes(self.graph, 'pheromone')\n",
        "        return {edge: pheromones.get(edge, 1.0) for edge in self.graph.edges()}\n",
        "    def select_path(self, ant, available_paths):\n",
        "        probabilities = [\n",
        "            ((self.pheromone_matrix[edge] ** self.alpha) * (1.0 / self.graph[edge[0]][edge[1]]['cost']) ** self.beta) for\n",
        "            edge in available_paths]\n",
        "        probabilities /= np.sum(probabilities)\n",
        "        selected_path = random.choices(available_paths, probabilities)[0]\n",
        "        return selected_path\n",
        "    def update_pheromones(self, ant_paths):\n",
        "        self.pheromone_matrix = {edge: (1.0 - self.evaporation_rate) * pheromone for edge, pheromone in\n",
        "                                 self.pheromone_matrix.items()}\n",
        "        for path in ant_paths:\n",
        "            total_cost = sum([1.0 / self.graph[edge[0]][edge[1]]['bandwidth'] for edge in path])\n",
        "            pheromone_deposit = 1.0 / total_cost\n",
        "            for i in range(len(path) - 1):\n",
        "                edge = path[i]\n",
        "                self.pheromone_matrix[edge] += pheromone_deposit\n",
        "                reverse_edge = (edge[1], edge[0])\n",
        "                self.pheromone_matrix[reverse_edge] = self.pheromone_matrix.get(reverse_edge, 0.0) + pheromone_deposit\n",
        "    def find_path(self, source, destination):\n",
        "        ant_paths = []\n",
        "        for _ in range(self.colony_size):\n",
        "            current_node = source\n",
        "            ant_path = []\n",
        "            while current_node != destination:\n",
        "                available_paths = [\n",
        "                    (current_node, neighbor) for neighbor in self.graph.successors(current_node) if\n",
        "                    (current_node, neighbor) not in ant_path]\n",
        "                if not available_paths:\n",
        "                    break\n",
        "                selected_path = self.select_path(_, available_paths)\n",
        "                ant_path.append(selected_path)\n",
        "                current_node = selected_path[1]\n",
        "            ant_paths.append(ant_path)\n",
        "        self.update_pheromones(ant_paths)\n",
        "        best_path = max(ant_paths, key=lambda path: sum([1.0 / self.graph[edge[0]][edge[1]]['bandwidth'] for edge in path]))\n",
        "        return best_path\n",
        "def plot_graph(graph, pheromone_matrix):\n",
        "    pos = nx.spring_layout(graph)\n",
        "    edge_labels = nx.get_edge_attributes(graph, 'pheromone')\n",
        "    nx.draw(graph, pos, with_labels=True, font_weight='bold', arrowsize=20)\n",
        "    nx.draw_networkx_edge_labels(graph, pos, edge_labels=edge_labels)\n",
        "    for edge, pheromone in pheromone_matrix.items():\n",
        "        plt.text(pos[edge[0]][0] + 0.02, pos[edge[0]][1] + 0.02, f'{pheromone:.2f}', fontsize=8)\n",
        "    plt.show()\n",
        "aco = AntColonyOptimization(G, colony_size=5, alpha=1.0, beta=2.0, evaporation_rate=0.5)\n",
        "source_node = 1\n",
        "destination_node = 5\n",
        "plot_graph(G, aco.pheromone_matrix)\n",
        "best_route = aco.find_path(source_node, destination_node)\n",
        "plot_graph(G, aco.pheromone_matrix)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "IneR6ehx8xGx",
        "outputId": "c36d49ee-4ced-43c2-b72c-356b2c97fcfd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!apt install libgraphviz-dev\n",
        "!pip install pygraphviz"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_gS0v2Lt9RYP",
        "outputId": "ab53994a-3e08-4125-81e6-e77611f9255c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree... Done\n",
            "Reading state information... Done\n",
            "The following additional packages will be installed:\n",
            "  libgail-common libgail18 libgtk2.0-0 libgtk2.0-bin libgtk2.0-common libgvc6-plugins-gtk\n",
            "  librsvg2-common libxdot4\n",
            "Suggested packages:\n",
            "  gvfs\n",
            "The following NEW packages will be installed:\n",
            "  libgail-common libgail18 libgraphviz-dev libgtk2.0-0 libgtk2.0-bin libgtk2.0-common\n",
            "  libgvc6-plugins-gtk librsvg2-common libxdot4\n",
            "0 upgraded, 9 newly installed, 0 to remove and 33 not upgraded.\n",
            "Need to get 2,433 kB of archives.\n",
            "After this operation, 7,694 kB of additional disk space will be used.\n",
            "Get:1 http://archive.ubuntu.com/ubuntu jammy/main amd64 libgtk2.0-common all 2.24.33-2ubuntu2 [125 kB]\n",
            "Get:2 http://archive.ubuntu.com/ubuntu jammy/main amd64 libgtk2.0-0 amd64 2.24.33-2ubuntu2 [2,037 kB]\n",
            "Get:3 http://archive.ubuntu.com/ubuntu jammy/main amd64 libgail18 amd64 2.24.33-2ubuntu2 [15.9 kB]\n",
            "Get:4 http://archive.ubuntu.com/ubuntu jammy/main amd64 libgail-common amd64 2.24.33-2ubuntu2 [132 kB]\n",
            "Get:5 http://archive.ubuntu.com/ubuntu jammy/universe amd64 libxdot4 amd64 2.42.2-6 [16.4 kB]\n",
            "Get:6 http://archive.ubuntu.com/ubuntu jammy/universe amd64 libgvc6-plugins-gtk amd64 2.42.2-6 [22.6 kB]\n",
            "Get:7 http://archive.ubuntu.com/ubuntu jammy/universe amd64 libgraphviz-dev amd64 2.42.2-6 [58.5 kB]\n",
            "Get:8 http://archive.ubuntu.com/ubuntu jammy/main amd64 libgtk2.0-bin amd64 2.24.33-2ubuntu2 [7,932 B]\n",
            "Get:9 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 librsvg2-common amd64 2.52.5+dfsg-3ubuntu0.2 [17.7 kB]\n",
            "Fetched 2,433 kB in 1s (2,259 kB/s)\n",
            "Selecting previously unselected package libgtk2.0-common.\n",
            "(Reading database ... 121749 files and directories currently installed.)\n",
            "Preparing to unpack .../0-libgtk2.0-common_2.24.33-2ubuntu2_all.deb ...\n",
            "Unpacking libgtk2.0-common (2.24.33-2ubuntu2) ...\n",
            "Selecting previously unselected package libgtk2.0-0:amd64.\n",
            "Preparing to unpack .../1-libgtk2.0-0_2.24.33-2ubuntu2_amd64.deb ...\n",
            "Unpacking libgtk2.0-0:amd64 (2.24.33-2ubuntu2) ...\n",
            "Selecting previously unselected package libgail18:amd64.\n",
            "Preparing to unpack .../2-libgail18_2.24.33-2ubuntu2_amd64.deb ...\n",
            "Unpacking libgail18:amd64 (2.24.33-2ubuntu2) ...\n",
            "Selecting previously unselected package libgail-common:amd64.\n",
            "Preparing to unpack .../3-libgail-common_2.24.33-2ubuntu2_amd64.deb ...\n",
            "Unpacking libgail-common:amd64 (2.24.33-2ubuntu2) ...\n",
            "Selecting previously unselected package libxdot4:amd64.\n",
            "Preparing to unpack .../4-libxdot4_2.42.2-6_amd64.deb ...\n",
            "Unpacking libxdot4:amd64 (2.42.2-6) ...\n",
            "Selecting previously unselected package libgvc6-plugins-gtk.\n",
            "Preparing to unpack .../5-libgvc6-plugins-gtk_2.42.2-6_amd64.deb ...\n",
            "Unpacking libgvc6-plugins-gtk (2.42.2-6) ...\n",
            "Selecting previously unselected package libgraphviz-dev:amd64.\n",
            "Preparing to unpack .../6-libgraphviz-dev_2.42.2-6_amd64.deb ...\n",
            "Unpacking libgraphviz-dev:amd64 (2.42.2-6) ...\n",
            "Selecting previously unselected package libgtk2.0-bin.\n",
            "Preparing to unpack .../7-libgtk2.0-bin_2.24.33-2ubuntu2_amd64.deb ...\n",
            "Unpacking libgtk2.0-bin (2.24.33-2ubuntu2) ...\n",
            "Selecting previously unselected package librsvg2-common:amd64.\n",
            "Preparing to unpack .../8-librsvg2-common_2.52.5+dfsg-3ubuntu0.2_amd64.deb ...\n",
            "Unpacking librsvg2-common:amd64 (2.52.5+dfsg-3ubuntu0.2) ...\n",
            "Setting up libxdot4:amd64 (2.42.2-6) ...\n",
            "Setting up librsvg2-common:amd64 (2.52.5+dfsg-3ubuntu0.2) ...\n",
            "Setting up libgtk2.0-common (2.24.33-2ubuntu2) ...\n",
            "Setting up libgtk2.0-0:amd64 (2.24.33-2ubuntu2) ...\n",
            "Setting up libgvc6-plugins-gtk (2.42.2-6) ...\n",
            "Setting up libgail18:amd64 (2.24.33-2ubuntu2) ...\n",
            "Setting up libgtk2.0-bin (2.24.33-2ubuntu2) ...\n",
            "Setting up libgail-common:amd64 (2.24.33-2ubuntu2) ...\n",
            "Setting up libgraphviz-dev:amd64 (2.42.2-6) ...\n",
            "Processing triggers for libc-bin (2.35-0ubuntu3.4) ...\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc.so.2 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc_proxy.so.2 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbb.so.12 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbbind.so.3 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_0.so.3 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_5.so.3 is not a symbolic link\n",
            "\n",
            "Processing triggers for man-db (2.10.2-1) ...\n",
            "Processing triggers for libgdk-pixbuf-2.0-0:amd64 (2.42.8+dfsg-1ubuntu0.2) ...\n",
            "Collecting pygraphviz\n",
            "  Downloading pygraphviz-1.12.tar.gz (104 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m104.9/104.9 kB\u001b[0m \u001b[31m2.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Installing backend dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "Building wheels for collected packages: pygraphviz\n",
            "  Building wheel for pygraphviz (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pygraphviz: filename=pygraphviz-1.12-cp310-cp310-linux_x86_64.whl size=168130 sha256=804b8fb4af6ec693e33b255bca9fb440e5ff7f35e43c1fb93219307d977dd4e5\n",
            "  Stored in directory: /root/.cache/pip/wheels/1d/ee/b5/a2f54f9e9b3951599c05dcce270ca85e472f8e6cec470e397a\n",
            "Successfully built pygraphviz\n",
            "Installing collected packages: pygraphviz\n",
            "Successfully installed pygraphviz-1.12\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import networkx as nx\n",
        "import matplotlib.pyplot as plt\n",
        "import random\n",
        "from concurrent.futures import ThreadPoolExecutor\n",
        "def initialize_pheromones(graph):\n",
        "    pheromones = {}\n",
        "    for edge in graph.edges():\n",
        "        pheromones[edge] = 1.0\n",
        "        reverse_edge = (edge[1], edge[0])\n",
        "        pheromones[reverse_edge] = 1.0\n",
        "    return pheromones\n",
        "def update_pheromones(pheromones, ant_paths, evaporation_rate=0.1):\n",
        "    for edge in pheromones:\n",
        "        pheromones[edge] *= (1 - evaporation_rate)\n",
        "    for path in ant_paths:\n",
        "        path_length = len(path)\n",
        "        for i in range(path_length - 1):\n",
        "            edge = (path[i], path[i + 1])\n",
        "            reverse_edge = (path[i + 1], path[i])\n",
        "            pheromones[edge] += 1.0 / path_length\n",
        "            pheromones[reverse_edge] += 1.0 / path_length\n",
        "def ant_colony_optimization_parallel(graph, source, destinations, num_ants=5, num_iterations=50, evaporation_rate=0.1):\n",
        "    pheromones = initialize_pheromones(graph)\n",
        "    best_path = None\n",
        "    best_path_length = float('inf')\n",
        "    def ant_path_iteration(ant_id):\n",
        "        current_node = source\n",
        "        path = [current_node]\n",
        "        while current_node not in destinations:\n",
        "            possible_edges = list(graph.edges(current_node))\n",
        "            probabilities = [pheromones[edge] for edge in possible_edges]\n",
        "            selected_edge = random.choices(possible_edges, probabilities)[0]\n",
        "            current_node = selected_edge[1]\n",
        "            path.append(current_node)\n",
        "        return path\n",
        "    with ThreadPoolExecutor() as executor:\n",
        "        ant_paths = list(executor.map(ant_path_iteration, range(num_ants)))\n",
        "    for path in ant_paths:\n",
        "        if len(path) < best_path_length:\n",
        "            best_path = path\n",
        "            best_path_length = len(path)\n",
        "    update_pheromones(pheromones, ant_paths, evaporation_rate)\n",
        "    return best_path\n",
        "def visualize_network(graph, best_route):\n",
        "    pos = nx.spring_layout(graph)\n",
        "    nx.draw(graph, pos, with_labels=True, node_size=1200, node_color='yellow', font_size=14, font_family='serif')\n",
        "    nx.draw_networkx_edges(graph, pos, edgelist=[(best_route[i], best_route[i + 1]) for i in range(len(best_route) - 1)],\n",
        "                           edge_color='red', width=2)\n",
        "    plt.show()\n",
        "G = nx.Graph()\n",
        "G.add_nodes_from(range(1, 6))\n",
        "G.add_edges_from([(1, 2), (1, 3), (2, 4), (3, 4), (3, 5), (4, 5)])\n",
        "source_node = 1\n",
        "destination_nodes = [4, 5]\n",
        "num_ants = 10\n",
        "num_iterations = 100\n",
        "evaporation_rate = 0.05\n",
        "best_route = ant_colony_optimization_parallel(G, source_node, destination_nodes, num_ants, num_iterations, evaporation_rate)\n",
        "visualize_network(G, best_route)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        },
        "id": "Hh8DkdzRwVgc",
        "outputId": "6a7b6a30-ce77-4350-f717-842a2d8bfa18"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**`Connectionless Network Routing`**"
      ],
      "metadata": {
        "id": "eneDhuHYxeEC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "class AntColony:\n",
        "    def __init__(self, graph, n_ants, alpha=1, beta=2, evaporation=0.5, Q=1):\n",
        "        self.graph = graph\n",
        "        self.pheromone = [[1] * len(graph[0]) for _ in range(len(graph))]\n",
        "        self.all_ants = []\n",
        "        self.n_ants = n_ants\n",
        "        self.alpha = alpha\n",
        "        self.beta = beta\n",
        "        self.evaporation = evaporation\n",
        "        self.Q = Q\n",
        "    def run(self, iterations):\n",
        "        for _ in range(iterations):\n",
        "            self.all_ants = []\n",
        "            for ant in range(self.n_ants):\n",
        "                self.all_ants.append(self._run_ant())\n",
        "            self._update_pheromone()\n",
        "    def _run_ant(self):\n",
        "        ant = Ant(self.graph)\n",
        "        while not ant.finished():\n",
        "            ant.move(self.pheromone, self.alpha, self.beta)\n",
        "        return ant\n",
        "    def _update_pheromone(self):\n",
        "        for i, row in enumerate(self.pheromone):\n",
        "            for j, col in enumerate(row):\n",
        "                evaporation = (1 - self.evaporation) * self.pheromone[i][j]\n",
        "                pheromone_change = 0\n",
        "                for ant in self.all_ants:\n",
        "                    if (i, j) in ant.visited_edges:\n",
        "                        pheromone_change += self.Q / ant.total_distance()\n",
        "                self.pheromone[i][j] = evaporation + pheromone_change\n",
        "class Ant:\n",
        "    def __init__(self, graph):\n",
        "        self.graph = graph\n",
        "        self.visited_nodes = set()\n",
        "        self.visited_edges = []\n",
        "        self.current_node = random.randint(0, len(graph) - 1)\n",
        "    def move(self, pheromone, alpha, beta):\n",
        "        allowed_nodes = [node for node in range(len(pheromone)) if node not in self.visited_nodes]\n",
        "        if not allowed_nodes:\n",
        "            return\n",
        "        probabilities = [self._calculate_probability(pheromone[self.current_node][node], self.graph[self.current_node][node], alpha, beta) for node in allowed_nodes]\n",
        "        selected_node = self._select_node(allowed_nodes, probabilities)\n",
        "        self.visited_edges.append((self.current_node, selected_node))\n",
        "        self.visited_nodes.add(selected_node)\n",
        "        self.current_node = selected_node\n",
        "    def _calculate_probability(self, pheromone, distance, alpha, beta):\n",
        "        distance = max(distance, 0.0001)\n",
        "        return (pheromone ** alpha) * ((1 / distance) ** beta)\n",
        "    def _select_node(self, nodes, probabilities):\n",
        "        selected_node = random.choices(nodes, weights=probabilities)[0]\n",
        "        return selected_node\n",
        "    def total_distance(self):\n",
        "        distance = 0\n",
        "        for edge in self.visited_edges:\n",
        "            distance += self.graph[edge[0]][edge[1]]\n",
        "        return distance\n",
        "    def finished(self):\n",
        "        return len(self.visited_nodes) == len(self.graph)\n",
        "graph = [\n",
        "    [0, 2, 3, 4],\n",
        "    [2, 0, 5, 6],\n",
        "    [3, 5, 0, 7],\n",
        "    [4, 6, 7, 0]\n",
        "]\n",
        "aco = AntColony(graph, n_ants=5)\n",
        "aco.run(iterations=100)\n",
        "best_ant = min(aco.all_ants, key=lambda ant: ant.total_distance())\n",
        "print(f\"Best path = {best_ant.visited_edges} \\nTotal distance = {best_ant.total_distance()}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ka8AjxQPzDG8",
        "outputId": "4ddd3069-e316-4d56-928e-e5ed3830d151"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best path = [(3, 3), (3, 2), (2, 0), (0, 1)] \n",
            "Total distance = 12\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "import pygraphviz as pgv\n",
        "from IPython.display import Image\n",
        "class AntColony:\n",
        "    def __init__(self, graph, n_ants, alpha=1, beta=2, evaporation=0.5, Q=1):\n",
        "        self.graph = graph\n",
        "        self.pheromone = [[1] * len(graph[0]) for _ in range(len(graph))]\n",
        "        self.all_ants = []\n",
        "        self.n_ants = n_ants\n",
        "        self.alpha = alpha\n",
        "        self.beta = beta\n",
        "        self.evaporation = evaporation\n",
        "        self.Q = Q\n",
        "    def run(self, iterations):\n",
        "        for _ in range(iterations):\n",
        "            self.all_ants = []\n",
        "            for ant in range(self.n_ants):\n",
        "                self.all_ants.append(self._run_ant())\n",
        "            self._update_pheromone()\n",
        "    def _run_ant(self):\n",
        "        ant = Ant(self.graph)\n",
        "        while not ant.finished():\n",
        "            ant.move(self.pheromone, self.alpha, self.beta)\n",
        "        return ant\n",
        "    def _update_pheromone(self):\n",
        "        for i, row in enumerate(self.pheromone):\n",
        "            for j, col in enumerate(row):\n",
        "                evaporation = (1 - self.evaporation) * self.pheromone[i][j]\n",
        "                pheromone_change = 0\n",
        "                for ant in self.all_ants:\n",
        "                    if (i, j) in ant.visited_edges:\n",
        "                        pheromone_change += self.Q / ant.total_distance()\n",
        "                self.pheromone[i][j] = evaporation + pheromone_change\n",
        "    def visualize_graph(self):\n",
        "        graph = pgv.AGraph(directed=True)\n",
        "        for i in range(len(self.graph)):\n",
        "            for j in range(len(self.graph[0])):\n",
        "                graph.add_edge(i, j, label=f\"{self.graph[i][j]} (pheromone: {self.pheromone[i][j]:.3f})\")\n",
        "        graph.layout(prog=\"dot\")\n",
        "        graph.draw(\"graph.png\")\n",
        "        return Image(filename=\"graph.png\")\n",
        "    def visualize_best_path(self, best_ant):\n",
        "        graph = pgv.AGraph(directed=True)\n",
        "        for edge in best_ant.visited_edges:\n",
        "            graph.add_edge(edge[0], edge[1], label=str(self.graph[edge[0]][edge[1]]))\n",
        "        graph.layout(prog=\"dot\")\n",
        "        graph.draw(\"best_path.png\")\n",
        "        return Image(filename=\"best_path.png\")\n",
        "class Ant:\n",
        "    def __init__(self, graph):\n",
        "        self.graph = graph\n",
        "        self.visited_nodes = set()\n",
        "        self.visited_edges = []\n",
        "        self.current_node = random.randint(0, len(graph) - 1)\n",
        "    def move(self, pheromone, alpha, beta):\n",
        "        allowed_nodes = [node for node in range(len(pheromone)) if node not in self.visited_nodes]\n",
        "        if not allowed_nodes:\n",
        "            return\n",
        "        probabilities = [self._calculate_probability(pheromone[self.current_node][node], self.graph[self.current_node][node], alpha, beta) for node in allowed_nodes]\n",
        "        selected_node = self._select_node(allowed_nodes, probabilities)\n",
        "        self.visited_edges.append((self.current_node, selected_node))\n",
        "        self.visited_nodes.add(selected_node)\n",
        "        self.current_node = selected_node\n",
        "    def _calculate_probability(self, pheromone, distance, alpha, beta):\n",
        "        distance = max(distance, 0.0001)\n",
        "        return (pheromone ** alpha) * ((1 / distance) ** beta)\n",
        "    def _select_node(self, nodes, probabilities):\n",
        "        selected_node = random.choices(nodes, weights=probabilities)[0]\n",
        "        return selected_node\n",
        "    def total_distance(self):\n",
        "        distance = 0\n",
        "        for edge in self.visited_edges:\n",
        "            distance += self.graph[edge[0]][edge[1]]\n",
        "        return distance\n",
        "    def finished(self):\n",
        "        return len(self.visited_nodes) == len(self.graph)\n",
        "graph = [\n",
        "    [0, 2, 3, 4],\n",
        "    [2, 0, 5, 6],\n",
        "    [3, 5, 0, 7],\n",
        "    [4, 6, 7, 0]\n",
        "]\n",
        "aco = AntColony(graph, n_ants=5)\n",
        "aco.visualize_graph()\n",
        "aco.run(iterations=100)\n",
        "best_ant = min(aco.all_ants, key=lambda ant: ant.total_distance())\n",
        "aco.visualize_best_path(best_ant)\n",
        "print(f\"Best path = {best_ant.visited_edges} \\nTotal distance = {best_ant.total_distance()}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pnQh6omxzjfU",
        "outputId": "f9185d6c-dc1f-4e23-8b20-b162fd708ea5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best path = [(1, 1), (1, 2), (2, 0), (0, 3)] \n",
            "Total distance = 12\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "![image.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "iXLoHvr2z5Ga"
      }
    }
  ]
}