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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# PSO for TSP"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Study the effect of the parameters $w, c_1, c_2$ on:\n",
"1) the quality of solutions to Euclidean TSP instances,\n",
"2) the speed of convergence.\n",
"\n",
"Show and interpret statistical plots for increasing number of points $n=100,200,\\ldots, 1000$.\n",
"\n",
"Give an overall conclusion where you summarise the effect of these 3 parametrs, and the recommended values."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"from scipy import spatial\n",
"import matplotlib.pyplot as plt\n",
"from sko.PSO import PSO_TSP"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Generation of points and distances matrix"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"n = 40\n",
"points = np.random.rand(n, 2) # generate points as coordinate (x,y) in the box [0,1] x [0,1]\n",
"distance_matrix = spatial.distance.cdist(points, points, metric='euclidean')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## PSO"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def calc_total_distance(cycle):\n",
" '''The objective function.\n",
" Input: cycle\n",
" Return: total distance\n",
" '''\n",
" num_points, = cycle.shape\n",
" return sum([distance_matrix[cycle[i % num_points], cycle[(i + 1) % num_points]] for i in range(num_points)])"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"pso_tsp = PSO_TSP(func=calc_total_distance,\n",
" n_dim=n,\n",
" size_pop=200,\n",
" max_iter=800,\n",
" w=0.8,\n",
" c1=0.1,\n",
" c2=0.1)\n",
"\n",
"best_points, best_distance = pso_tsp.run()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"best_distance [4.96612188]\n"
]
}
],
"source": [
"print('best_distance', best_distance)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# %% plot\n",
"fig, ax = plt.subplots(1, 2)\n",
"best_points_ = np.concatenate([best_points, [best_points[0]]])\n",
"best_points_coordinate = points[best_points_, :]\n",
"ax[0].plot(best_points_coordinate[:, 0], best_points_coordinate[:, 1], 'o-r')\n",
"ax[1].plot(pso_tsp.gbest_y_hist)\n",
"ax[0].set_aspect('equal')\n",
"ax[1].set_aspect(80)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"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": "3.11.1"
},
"toc": {
"base_numbering": 1,
"nav_menu": {},
"number_sections": false,
"sideBar": true,
"skip_h1_title": false,
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": false,
"toc_position": {},
"toc_section_display": true,
"toc_window_display": false
},
"vscode": {
"interpreter": {
"hash": "6d1e45cadc3597bb8b6600530fbdf8c3eefe919a24ef54d9d32b318795b772e0"
}
}
},
"nbformat": 4,
"nbformat_minor": 4
}