examples/tsp.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Traveling Salesman Problem\n",
    "---\n",
    "Description:\n",
    "\n",
    "- Optimization (combinatorial)\n",
    "- Single-objective\n",
    "- Constraints (yes)\n",
    "---\n",
    "\n",
    "The travelling salesman problem, asks the following question: Given a list of cities and the distances between each\n",
    "pair of cities, what is the shortest possible route that visits each city exactly once and returns to the origin city?\n",
    "\n",
    "\n",
    "https://en.wikipedia.org/wiki/Travelling_salesman_problem"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First we import python libraries and set up the directory of our code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os, sys, copy\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import namedtuple\n",
    "\n",
    "PROJECT_DIR = os.path.abspath('..')\n",
    "sys.path.append(PROJECT_DIR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Here we import all our custom GA code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import main classes.\n",
    "from pygenalgo.genome.gene import Gene\n",
    "from pygenalgo.genome.chromosome import Chromosome\n",
    "from pygenalgo.engines.standard_ga import StandardGA\n",
    "\n",
    "# Import Selection Operator(s).\n",
    "from pygenalgo.operators.selection.linear_rank_selector import LinearRankSelector\n",
    "\n",
    "# Import Crossover Operator(s).\n",
    "from pygenalgo.operators.crossover.order_crossover import OrderCrossover\n",
    "\n",
    "# Import Mutation Operator(s).\n",
    "from pygenalgo.operators.mutation.swap_mutator import SwapMutator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We create a small data sructure to hold the city information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data structure to represent a city on a grid.\n",
    "City = namedtuple(\"City\", [\"Name\", \"x\", \"y\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Then we create a list of cities on a grid with (x, y) coordinates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define n_cities on a grid [0, 15] x [0, 15]\n",
    "rome   = City(\"Rome\"  , 3,  2)\n",
    "athens = City(\"Athens\", 7,  3)\n",
    "lisbon = City(\"Lisbon\", 14, 2)\n",
    "madrid = City(\"Madrid\", 3,  7)\n",
    "berlin = City(\"Berlin\", 12, 7)\n",
    "paris  = City(\"Paris\" , 2, 14)\n",
    "london = City(\"London\", 7, 12)\n",
    "moscow = City(\"Moscow\", 12,12)\n",
    "dublin = City(\"Dublin\", 6,  9)\n",
    "cairo  = City(\"Cairo\" , 10, 8)\n",
    "\n",
    "# Put all the cities in a list.\n",
    "city_list = [rome, athens, lisbon, madrid, berlin,\n",
    "             paris, london, moscow, dublin, cairo]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lets plot the list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "for city in city_list:\n",
    "    ax.plot(city.x, city.y, 'o')\n",
    "    ax.annotate(f'  {city.Name}', xy=(city.x, city.y))\n",
    "# _end_for_\n",
    "\n",
    "ax.grid()\n",
    "\n",
    "ax.set_xlim([0, 16])\n",
    "ax.set_ylim([0, 16])\n",
    "\n",
    "plt.title(\"Map of Cities\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the fitness function.\n",
    "def fun_TSP(individual: Chromosome, f_min: bool = True):\n",
    "    \"\"\"\n",
    "    This function computes the total distance travelled from all cities.\n",
    "    We assume (hard constraint) that the order that they cities appear\n",
    "    in the genome list, is the same order the traveler is visiting them.\n",
    "\n",
    "    When we reach the end of the list we make one extra calculation from\n",
    "    the last entry of the list to the first: [-1] -> [0].\n",
    "\n",
    "    To discourage loops if two consecutive cities are identical we add a\n",
    "    penalty to the total distance. However, this should not happen if we\n",
    "    use only swap mutation operator.\n",
    "    \"\"\"\n",
    "\n",
    "    # Compute the distance.\n",
    "    total_distance = 0.0\n",
    "\n",
    "    # Get the size of the genome.\n",
    "    N = len(individual.genome)\n",
    "    \n",
    "    # Iterate through all the cities.\n",
    "    for i, city_a in enumerate(individual.genome):\n",
    "\n",
    "        # The next index ('j') should point at the\n",
    "        # begining of the list, once we reach at the end.\n",
    "        j = i + 1 if i < N-1 else 0\n",
    "\n",
    "        # Get the next city in the list.\n",
    "        city_b = individual.genome[j]\n",
    "\n",
    "        # If the cities are the same, add penatly.\n",
    "        if city_a == city_b:\n",
    "            total_distance += 15.0\n",
    "        else:\n",
    "\n",
    "            # Otherwise compute their distance.\n",
    "            x_a, y_a = city_a.value.x, city_a.value.y\n",
    "            x_b, y_b = city_b.value.x, city_b.value.y\n",
    "\n",
    "            # Compute the Euclidean distance.\n",
    "            total_distance += np.sqrt((x_a - x_b)**2 + ((y_a - y_b)**2))\n",
    "        # _end_if_\n",
    "    \n",
    "    # _end_for_\n",
    "\n",
    "    # Condition for termination.\n",
    "    solution_found = False\n",
    "\n",
    "    # Assign the fitness value (check for minimization).\n",
    "    fit_value = -total_distance if f_min else total_distance\n",
    "    \n",
    "    # Return the solution tuple.\n",
    "    return fit_value, solution_found\n",
    "# _end_def_\n",
    "\n",
    "# Set a random generator.\n",
    "rng = np.random.default_rng()\n",
    "\n",
    "# Random function: Select randomly a city from the list.\n",
    "# In practice this will not be used here because of the\n",
    "# choice we made with the swap mutator.\n",
    "rand_fc = lambda: city_list[rng.integers(0, len(city_list))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Important note\n",
    "\n",
    "These types of problems (like the TSP) require special crossover/mutation operators for permutations, in order to avoid creating invalid offsprings. The requirement is that all the genes from the initial population have to be present exactly once in the offsprings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the number of chromosomes.\n",
    "N = 100\n",
    "\n",
    "# Initial population.\n",
    "population = []\n",
    "\n",
    "# Make an identical copy of the city list.\n",
    "init_list = copy.deepcopy(city_list)\n",
    "\n",
    "for i in range(N):\n",
    "    \n",
    "    # Shuffle the list before each initialization\n",
    "    # to provide a different order of the cities.\n",
    "    rng.shuffle(init_list)\n",
    "\n",
    "    # Create the Chromosome and add it into the population.\n",
    "    population.append(Chromosome([Gene(c, rand_fc) for c in init_list],\n",
    "                                 np.nan, True))\n",
    "# _end_for_\n",
    "\n",
    "# Create the StandardGA object that will carry on the optimization.\n",
    "test_GA = StandardGA(initial_pop=population,\n",
    "                     fit_func=fun_TSP,\n",
    "                     select_op=LinearRankSelector(),\n",
    "                     mutate_op=SwapMutator(0.2),\n",
    "                     crossx_op=OrderCrossover(0.8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial Avg. Fitness = -80.5953.\n",
      "Final   Avg. Fitness = -52.0628.\n",
      "Elapsed time: 18.784 seconds.\n"
     ]
    }
   ],
   "source": [
    "test_GA(epochs=500, elitism=True, verbose=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Display the final solution along with the plot of the cities visited."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum (distance) Found: 48.68138\n",
      "\n",
      "0 : City(Name='Madrid', x=3, y=7)\n",
      "1 : City(Name='Dublin', x=6, y=9)\n",
      "2 : City(Name='Paris', x=2, y=14)\n",
      "3 : City(Name='London', x=7, y=12)\n",
      "4 : City(Name='Moscow', x=12, y=12)\n",
      "5 : City(Name='Cairo', x=10, y=8)\n",
      "6 : City(Name='Berlin', x=12, y=7)\n",
      "7 : City(Name='Lisbon', x=14, y=2)\n",
      "8 : City(Name='Athens', x=7, y=3)\n",
      "9 : City(Name='Rome', x=3, y=2)\n"
     ]
    }
   ],
   "source": [
    "# Extract the optimal solution from the GA.\n",
    "optimal_solution = test_GA.best_chromosome()\n",
    "\n",
    "# Extract the fitness value from the optimal solution.\n",
    "optimal_fit, _ = fun_TSP(optimal_solution, f_min=False)\n",
    "\n",
    "# Display the (final) optimal value.\n",
    "print(f\"Minimum (distance) Found: {optimal_fit:.5f}\\n\")\n",
    "\n",
    "# Display each gene value separately.\n",
    "for i, xi in enumerate(optimal_solution.genome):\n",
    "    print(f\"{i} : {xi.value}\")\n",
    "# _end_for_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "for city in city_list:\n",
    "    ax.plot(city.x, city.y, 'o')\n",
    "    ax.annotate(f'  {city.Name}', xy=(city.x, city.y))\n",
    "# _end_for_\n",
    "\n",
    "# Get the size of the genome.\n",
    "N = len(optimal_solution.genome)\n",
    "\n",
    "# Iterate through all the cities.\n",
    "for i, city_a in enumerate(optimal_solution.genome):\n",
    "\n",
    "    # The next index 'j' should point at the\n",
    "    # begining of the list, once we reach the end.\n",
    "    j = i + 1 if i < N-1 else 0\n",
    "\n",
    "    # Get the next city in the list.\n",
    "    city_b = optimal_solution.genome[j]\n",
    "\n",
    "    x_a, y_a = city_a.value.x, city_a.value.y\n",
    "    x_b, y_b = city_b.value.x, city_b.value.y\n",
    "\n",
    "    ax.annotate(\"\", xy=(x_a, y_a), xytext=(x_b, y_b),\n",
    "                arrowprops=dict(arrowstyle=\"<-\"))\n",
    "# _end_for_\n",
    "\n",
    "ax.grid()\n",
    "\n",
    "ax.set_xlim([0, 16])\n",
    "ax.set_ylim([0, 16])\n",
    "\n",
    "plt.title(\"Optimal Solution Found\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "SOMap",
   "language": "python",
   "name": "somap"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}