TSP.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPrDukkJADSlrGX89adH5S7",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Ananthzeke/Optimization-techniques/blob/main/TSP.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## TSP\n"
      ],
      "metadata": {
        "id": "N-jS-sBZbd3i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import networkx as nx\n",
        "import pandas as pd"
      ],
      "metadata": {
        "id": "3gFRPMAoa3Nq"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def generate_random_weighted_graph(n,low,high):\n",
        "    g = nx.generators.complete_graph(n)\n",
        "    # add random weights to the graph\n",
        "    g.add_weighted_edges_from([(a,b,np.random.randint(low,high)) for a,b in g.edges()])\n",
        "    return g\n"
      ],
      "metadata": {
        "id": "8sddx51ga7r7"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.random.seed(3)\n",
        "g = generate_random_weighted_graph(7,1,20)\n"
      ],
      "metadata": {
        "id": "ihUctWJ1bAS2"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nx.draw_networkx(g)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "6HGlw2d1cybu",
        "outputId": "b55eef2c-48e3-4d30-bdd5-efb67aba1668"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "pos=nx.spring_layout(g) \n",
        "nx.draw_networkx(g,pos)\n",
        "labels = nx.get_edge_attributes(g,'weight')\n",
        "nx.draw_networkx_edge_labels(g,pos,edge_labels=labels)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "Wroy7yGneI9j",
        "outputId": "022d6c0c-d061-4241-f4f4-5446b2eed0d9"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Intialization"
      ],
      "metadata": {
        "id": "Wylx3GOdbi5c"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Nearest Neighbour Initialization\n",
        "  - Intialize $i = 1$\n",
        "  - Randomly select a node\n",
        "  - Find the node  closest to $n(i)$ that has not yet been assigned to an element of $n$ and assign it to $n(i+1)$\n",
        "  - $i=i+1$\n",
        "  - If $i==n$:\n",
        "    - terminate \n",
        "  - else:\n",
        "    - Go to Step 3"
      ],
      "metadata": {
        "id": "i6WQYRfLmqbP"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "MDQnARLXZAF4"
      },
      "outputs": [],
      "source": [
        "def nearest_neighbour_initialization(g,closed_tour=False):\n",
        "    curr_node = np.random.choice(g.nodes) \n",
        "    path = [curr_node]\n",
        "    not_visited = set(g.nodes)-{curr_node}\n",
        "    while not_visited:\n",
        "        not_visited_neighbours = not_visited&set(g.neighbors(curr_node))\n",
        "        key =lambda x: g[curr_node][x][\"weight\"]\n",
        "        curr_node = min(not_visited_neighbours,key = key) \n",
        "        path.append(curr_node)\n",
        "        not_visited.remove(curr_node)\n",
        "    if closed_tour:\n",
        "        path.append(path[0])\n",
        "    return path"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nearest_neighbour_initialization(g)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oUP49QjFbGt1",
        "outputId": "a673ab2c-3e0f-4d1c-c4a5-d349f5f89804"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[5, 1, 4, 0, 2, 6, 3]"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Shortest edge intialization"
      ],
      "metadata": {
        "id": "ummv0bduoaLm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from collections import defaultdict"
      ],
      "metadata": {
        "id": "je30Z7uq3H37"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def has_cycle(g):\n",
        "    try:\n",
        "        nx.find_cycle(g)\n",
        "    except nx.NetworkXNoCycle:\n",
        "        return False\n",
        "    return True\n",
        "\n",
        "def get_path_from_edges(edges,closed_tour=False):\n",
        "    path_graph = nx.Graph(edges)\n",
        "    # if it is an open tour start from a node with a single degree\n",
        "    curr = min(path_graph.nodes,key=path_graph.degree) \n",
        "    path,visited = [curr],{curr}\n",
        "    while len(path)<len(path_graph):\n",
        "        curr = (set(path_graph.neighbors(curr))-visited).pop()\n",
        "        visited.add(curr)\n",
        "        path.append(curr)\n",
        "    if closed_tour:\n",
        "        path.append(path[0])\n",
        "    return path"
      ],
      "metadata": {
        "id": "4J4AE2hsoeht"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def shortest_edge_initialization(g,method=\"greedy\", closed_tour = False):\n",
        "    edge_list = set(g.edges)\n",
        "    times_visited  = defaultdict(int)\n",
        "    tour = set() \n",
        "    max_tour_len = len(g) if closed_tour else len(g)-1\n",
        "    key = nx.get_edge_attributes(g,\"weight\").get\n",
        "    while len(tour)<max_tour_len:\n",
        "       u,v = min(edge_list, key=key)\n",
        "       times_visited[u]+=1\n",
        "       times_visited[v]+=1\n",
        "       tour.add((u,v))\n",
        "       edge_list.remove((u,v))\n",
        "       for u,v in set(edge_list):\n",
        "            if (\n",
        "                (has_cycle(nx.Graph(tour|{(u,v)})) and len(tour) != len(g)-1)\n",
        "                or times_visited[u] ==2 or times_visited[v] ==2\n",
        "\n",
        "            ):\n",
        "                edge_list.remove((u,v))\n",
        "\n",
        "    return get_path_from_edges(tour,closed_tour=closed_tour)"
      ],
      "metadata": {
        "id": "bBCffwbRouD3"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "shortest_edge_initialization(g)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A-p8zjOT29ea",
        "outputId": "b4c48f65-c761-42b3-fd3b-981482439645"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 5, 6, 2, 0, 4, 3]"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Cross over"
      ],
      "metadata": {
        "id": "4eHpo5zV3K8w"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Partially Matched Crossover"
      ],
      "metadata": {
        "id": "5AXK25DJ5yh7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def partially_matched_crossover(p1,p2):\n",
        "    pt = np.random.randint(1,len(p1)-1) # crossover point\n",
        "    c1 = p1[:pt] + p2[pt:]\n",
        "    c2 = p2[:pt] + p1[pt:]\n",
        "    m1=set(p1)-set(c1)\n",
        "    m2=set(p2)-set(c2)\n",
        "    \n",
        "    if m1 or m2:\n",
        "        c1=pd.Series(c1)\n",
        "        c1[c1.duplicated()]=list(m1)\n",
        "        c2=pd.Series(c2)\n",
        "        c2[c2.duplicated()]=list(m2)\n",
        "        c1=c1.to_list()\n",
        "        c2=c2.to_list()\n",
        "        \n",
        "    return c1,c2"
      ],
      "metadata": {
        "id": "ayC8tcbw3fVk"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "parents=[[1, 2, 0, 4, 6, 5, 3], [0, 1, 4, 2, 6, 5, 3]]\n",
        "print(partially_matched_crossover(*parents))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vV4x64fO3hnP",
        "outputId": "e464d4bb-48f6-4380-8758-16971fb33013"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "([1, 2, 0, 4, 6, 5, 3], [0, 1, 4, 2, 6, 5, 3])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Order Crossover"
      ],
      "metadata": {
        "id": "1BfeMSuR5oHD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def order_crossover(p1,p2):\n",
        "    start = np.random.randint(0,len(p1)-1)\n",
        "    end = np.random.randint(start+1,len(p1) if start !=0 else len(p1)-1)\n",
        "    def fill_blanks(p1,p2,s,e):\n",
        "        \n",
        "        unvisited_nodes = p2.copy()\n",
        "        for node in p1[s:e]:\n",
        "            unvisited_nodes.remove(node)\n",
        "\n",
        "        c = p1.copy()\n",
        "        for i in range(len(p1)):\n",
        "            if i<s or i>=e:\n",
        "                c[i] = unvisited_nodes.pop(0)\n",
        "        return c\n",
        "\n",
        "    c1 = fill_blanks(p1,p2,start,end)\n",
        "    c2 = fill_blanks(p2,p1,start,end)\n",
        "    return c1,c2"
      ],
      "metadata": {
        "id": "KW_dStSc56DH"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "order_crossover(*parents)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BFj3Z4SF6CuV",
        "outputId": "b19b4c18-f04d-4235-cfea-0e6d9f3c8376"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "([1, 2, 0, 4, 6, 5, 3], [0, 1, 4, 2, 6, 5, 3])"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Mutation"
      ],
      "metadata": {
        "id": "cwXp30Nf6LF2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Inversion"
      ],
      "metadata": {
        "id": "PpmBoBzs6NGk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def inversion_mutation(p):\n",
        "    start = np.random.randint(0,len(p)-1)\n",
        "    end = np.random.randint(start+1,len(p)+1)\n",
        "    subtour = p[start:end]\n",
        "    c = p.copy()\n",
        "    for i in range(start,end):\n",
        "        c[i] = subtour.pop()\n",
        "    return c"
      ],
      "metadata": {
        "id": "DwWoLL7h6SIB"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "p=[2, 0, 4, 1, 3, 6, 5]\n",
        "inversion_mutation(p)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2TzXiVeU6T-Y",
        "outputId": "d0f286f4-bb21-4570-a1db-5787ad385296"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[2, 3, 1, 4, 0, 6, 5]"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Insertion"
      ],
      "metadata": {
        "id": "QvTDOCFV6z4Y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def insertion_mutation(p):\n",
        "    i = np.random.randint(1,len(p))\n",
        "    k = np.random.randint(0,len(p)-1)\n",
        "    c = p.copy()\n",
        "    print(c,i,k)\n",
        "    c.insert(k,c.pop(i))\n",
        "    return c"
      ],
      "metadata": {
        "id": "8uTt185c7A7h"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "insertion_mutation(p)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E7LLWu237Bm3",
        "outputId": "ee2bbe85-ecb2-4b2c-824f-63c5388579b0"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[2, 0, 4, 1, 3, 6, 5] 3 4\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[2, 0, 4, 3, 1, 6, 5]"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    }
  ]
}