{
"cells": [
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import math as m\n",
"import numpy as np\n",
"from numpy import random as r\n",
"import time\n",
"import matplotlib.pyplot as plt\n",
"from mpl_toolkits import mplot3d\n",
"import copy as cp\n",
"import plotly\n",
"import plotly.graph_objs as go\n",
"\n",
"# Configure Plotly to be rendered inline in the notebook.\n",
"plotly.offline.init_notebook_mode()\n",
"\n",
"%matplotlib inline\n",
"r.seed(int(time.time()))\n",
"route = list()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"class settings:\n",
" iter_max = 10000\n",
" pop_size = 100\n",
" dimensions = 2\n",
" c1 = 2\n",
" c2 = 2\n",
" neededQuality = 1000000\n",
" objective = 0\n",
" sigma = 0.001"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"def func(Sc):\n",
" x1 = Sc[0]\n",
" x2 = Sc[1]\n",
" return m.sqrt(x1**2+x2**2)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"def Quality(Sc,objective):\n",
" func_output = func(Sc)\n",
" if type(func_output) == list:\n",
" error = [func_output[i]-objective[i] for i in range(len(func_output))]\n",
" else:\n",
" error = func_output - objective\n",
" return 1/abs(error)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"def tweak(St,twprob,sigma,high,low):\n",
" for i in range(len(St)):\n",
" if twprob > r.random():\n",
" while True:\n",
" n = r.normal(loc=0, scale=sigma)\n",
" if (high > St[i]+n) and (low < St[i]+n):\n",
" St[i]+=n\n",
" break\n",
" return np.array(St)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"def move(p,world,best):\n",
" v = p.v + world.c1 * r.random() * (p.best - p.params) \\\n",
" + world.c2 * r.random() * (best.params - p.params)\n",
" \n",
" return v"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"def simulatedAnnealing(high,low,t,twprob,world):\n",
" route = list()\n",
" #initialize the particles\n",
" particles = []\n",
" for i in range(world.pop_size):\n",
" p = Particle()\n",
" p.params = np.array([r.uniform(-100,100) for i in range(world.dimensions)])\n",
" p.fitness = 0.0\n",
" p.v = 0.0\n",
" particles.append(p)\n",
" \n",
" Best = cp.deepcopy(particles[0])\n",
" Q = 0\n",
" route.append(cp.deepcopy(Best.params[:]))\n",
" for i in range(world.iter_max):\n",
" for p in particles:\n",
" Q = Quality(p.params,world.objective)\n",
" try:\n",
" prob = m.e**((Q-p.fitness)/t)\n",
" except:\n",
" pass\n",
" \n",
" if (Q > p.fitness) or (r.random() < prob):\n",
" p.fitness = Q\n",
" p.best = p.params\n",
" \n",
" if t > 0.01:\n",
" t-= t/10\n",
" \n",
" if Q > Best.fitness:\n",
" Best = cp.deepcopy(p)\n",
" route.append(Best.params[:])\n",
" print('////////////\\n////////////\\n******new Best: {}'.format(Best.params))\n",
" \n",
" v = move(p,world,Best)\n",
" p.params += v\n",
" p.params = tweak(cp.deepcopy(p.params),twprob,world.sigma,high,low)\n",
" \n",
" if Q > world.neededQuality:\n",
" break\n",
" \n",
" return Best, route"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"class Particle:\n",
" pass"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"#initialize the particles\n",
"world = settings()\n",
"particles = []\n",
"for i in range(world.pop_size):\n",
" p = Particle()\n",
" p.params = np.array([r.random() for i in range(world.dimensions)])\n",
" p.fitness = 0.0\n",
" p.v = 0.0\n",
" particles.append(p)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
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},
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mworld\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msettings\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msimulatedAnnealing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhigh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlow\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mt\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtwprob\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mworld\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mworld\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m\u001b[0m in \u001b[0;36msimulatedAnnealing\u001b[0;34m(high, low, t, twprob, world)\u001b[0m\n\u001b[1;32m 35\u001b[0m \u001b[0mv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mworld\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mBest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 36\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparams\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 37\u001b[0;31m \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparams\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtweak\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtwprob\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mworld\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msigma\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mhigh\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlow\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 38\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 39\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mQ\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mworld\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mneededQuality\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m\u001b[0m in \u001b[0;36mtweak\u001b[0;34m(St, twprob, sigma, high, low)\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtwprob\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnormal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscale\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msigma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mhigh\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mSt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlow\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mSt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mSt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+=\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"source": [
"world = settings()\n",
"a, path = simulatedAnnealing(high = 100,low = -100,t =100, twprob = 0.2, world = world)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([-8.76862994e-07, -1.26566209e-06])"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a.params"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"h1 = [i[0] for i in path]\n",
"h2 = [i[1] for i in path]\n",
"plt.plot(h1,h2)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
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"config": {
"linkText": "Export to plot.ly",
"plotlyServerURL": "https://plot.ly",
"showLink": false
},
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