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demotexteditor.html
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<!DOCTYPE html>
<html lang="en">
<head>
<title>ACE in Action</title>
<style type="text/css" media="screen">
#editor {
position: absolute;
top: 0;
right: 0;
bottom: 0;
left: 0;
}
</style>
</head>
<body>
<!-- <div id="editor">function foo(items) {
var x = "All this is syntax highlighted";
return x;
}</div> -->
<div id="editor">
# ANT COLONY OPTIMIZATION
import math
import random
import webbrowser
from tkinter import *
from tkinter.ttk import *
import time
import matplotlib
matplotlib.use("TkAgg")
from matplotlib.figure import Figure
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.animation import FuncAnimation
import matplotlib.animation as animation
#_nodes = [(random.uniform(-400, 400), random.uniform(-400, 400)) for _ in range(0, 25)]
#xx = [i[0] for i in _nodes]
#yy = [i[1] for i in _nodes]
class SolveTSPUsingACO:
class Edge:
def __init__(self, a, b, weight, initial_pheromone):
self.a = a
self.b = b
self.weight = weight
self.pheromone = initial_pheromone
class Ant:
def __init__(self, alpha, beta, num_nodes, edges):
self.alpha = alpha
self.beta = beta
self.num_nodes = num_nodes
self.edges = edges
self.tour = None
self.distance = 0.0
def _select_node(self):
roulette_wheel = 0.0
unvisited_nodes = [node for node in range(self.num_nodes) if node not in self.tour]
heuristic_total = 0.0
for unvisited_node in unvisited_nodes:
heuristic_total += self.edges[self.tour[-1]][unvisited_node].weight
for unvisited_node in unvisited_nodes:
roulette_wheel += pow(self.edges[self.tour[-1]][unvisited_node].pheromone, self.alpha) * \
pow((heuristic_total / self.edges[self.tour[-1]][unvisited_node].weight), self.beta)
random_value = random.uniform(0.0, roulette_wheel)
wheel_position = 0.0
for unvisited_node in unvisited_nodes:
wheel_position += pow(self.edges[self.tour[-1]][unvisited_node].pheromone, self.alpha) * \
pow((heuristic_total / self.edges[self.tour[-1]][unvisited_node].weight), self.beta)
if wheel_position >= random_value:
return unvisited_node
def find_tour(self):
self.tour = [random.randint(0, self.num_nodes - 1)]
while len(self.tour) < self.num_nodes:
self.tour.append(self._select_node())
return self.tour
def get_distance(self):
self.distance = 0.0
for i in range(self.num_nodes):
self.distance += self.edges[self.tour[i]][self.tour[(i + 1) % self.num_nodes]].weight
return self.distance
def __init__(self, mode='ACS', colony_size=10, elitist_weight=1.0, min_scaling_factor=0.001, alpha=1.0, beta=3.0,
rho=0.1, pheromone_deposit_weight=1.0, initial_pheromone=1.0, steps=100, nodes=None, labels=None):
self.mode = mode
self.colony_size = colony_size
self.elitist_weight = elitist_weight
self.min_scaling_factor = min_scaling_factor
self.rho = rho
self.pheromone_deposit_weight = pheromone_deposit_weight
self.steps = steps
self.num_nodes = len(nodes)
self.nodes = nodes
self.tours=[]
cities=self.nodes
if labels is not None:
self.labels = labels
else:
self.labels = range(1, self.num_nodes + 1)
self.edges = [[None] * self.num_nodes for _ in range(self.num_nodes)]
for i in range(self.num_nodes):
for j in range(i + 1, self.num_nodes):
self.edges[i][j] = self.edges[j][i] = self.Edge(i, j, math.sqrt(
pow(self.nodes[i][0] - self.nodes[j][0], 2.0) + pow(self.nodes[i][1] - self.nodes[j][1], 2.0)),
initial_pheromone)
self.ants = [self.Ant(alpha, beta, self.num_nodes, self.edges) for _ in range(self.colony_size)]
self.global_best_tour = None
self.global_best_tours =[]
self.global_best_distance = float("inf")
def _add_pheromone(self, tour, distance, weight=1.0):
pheromone_to_add = self.pheromone_deposit_weight / distance
for i in range(self.num_nodes):
self.edges[tour[i]][tour[(i + 1) % self.num_nodes]].pheromone += weight * pheromone_to_add
def _elitist(self):
path=[]
cost=[]
for step in range(self.steps):
self.tours=[]
for ant in self.ants:
self._add_pheromone(ant.find_tour(), ant.get_distance())
#self.tours.append(ant.tour)
l=[]
for i in ant.tour:
l.append(self.nodes[i])
self.tours.append(l)
if ant.distance < self.global_best_distance:
self.global_best_tour = ant.tour
self.global_best_distance = ant.distance
self._add_pheromone(self.global_best_tour, self.global_best_distance, weight=self.elitist_weight)
path.append(self.global_best_tour)
cost.append(self.global_best_distance)
#self.plottour(self.tours,self.global_best_tour)
#l=[]
#x=0
#for i in self.tours:
#l.append([])
#x=0;
#for j in i:
#l[x].append(self.nodes[j])
#x+=1
#plottour(l)
for i in range(self.num_nodes):
for j in range(i + 1, self.num_nodes):
self.edges[i][j].pheromone *= (1.0 - self.rho)
self.bhagwan_bharose(path,cost)
def run(self):
print('Started : {0}'.format(self.mode))
self._elitist()
print('Ended : {0}'.format(self.mode))
print('Sequence : <- {0} ->'.format(' - '.join(str(self.labels[i]) for i in self.global_best_tour)))
print('Total distance travelled to complete the tour : {0}\n'.format(round(self.global_best_distance, 2)))
def plot(self):
webbrowser.open_new_tab('a.html')
def bhagwan_bharose(self,paths, costs,animation_filename="ACO_TSP_Berlin52"):
global xx,yy
fig = plt.figure(figsize=(12, 8))
gs = fig.add_gridspec(2, 3, wspace=0.45, hspace=0.35)
# Plot berlin52 points
ax1 = fig.add_subplot(gs[0, 0])
ax1.scatter(xx,yy, c='r', edgecolors='black')
ax1.set_xlabel('x (kms)')
ax1.set_ylabel('y (kms)')
ax1.set_title('Cities', fontweight='bold', pad=10)
# Plot min cost found per iteration
ax2 = fig.add_subplot(gs[1, 0])
costline, = ax2.plot([], [])
ax2.set_xlabel('Iterations')
ax2.set_ylabel('Distance (kms)')
ax2.set_ylim(3000, 5500)
ax2.set_xlim(0, len(costs)+1)
ax2.set_title('Best path distance in every iteration', fontweight='bold', pad=10)
# Plot best path found per iteration
ax3 = fig.add_subplot(gs[:, 1:])
ax3.scatter(xx, yy, c='r', edgecolors='black')
ax3.set_xlabel('x (kms)')
ax3.set_ylabel('y (kms)')
title = ax3.set_title('Best Path Cost', fontweight='bold', fontsize=13, pad=10)
def init():
pass
def animate(iter):
if iter < len(costs):
costline.set_data(list(range(1, iter+2)), costs[:iter+1])
# print(costline)
if iter > 0 and iter <= len(costs):
ax3.lines.pop(0)
if iter < len(costs):
x = [xx[i] for i in paths[iter]]
y = [yy[j] for j in paths[iter]]
x.append(x[0])
y.append(y[0])
ax3.plot(x, y, '--', alpha=0.9)
title.set_text(f"Best Path Cost : {round(costs[iter], 2)} kms")
elif iter == len(costs):
x = [se<!DOCTYPE html>
<html lang="en">
<head>
<title>ACE in Action</title>
<style type="text/css" media="screen">
#editor {
position: absolute;
top: 0;
right: 0;
bottom: 0;
left: 0;
}
</style>
</head>
<body>
<!-- <div id="editor">function foo(items) {
var x = "All this is syntax highlighted";
return x;
}</div> -->
<div id="editor">
# ANT COLONY OPTIMIZATION
import math
import random
import webbrowser
from tkinter import *
from tkinter.ttk import *
import time
import matplotlib
matplotlib.use("TkAgg")
from matplotlib.figure import Figure
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.animation import FuncAnimation
import matplotlib.animation as animation
#_nodes = [(random.uniform(-400, 400), random.uniform(-400, 400)) for _ in range(0, 25)]
#xx = [i[0] for i in _nodes]
#yy = [i[1] for i in _nodes]
class SolveTSPUsingACO:
class Edge:
def __init__(self, a, b, weight, initial_pheromone):
self.a = a
self.b = b
self.weight = weight
self.pheromone = initial_pheromone
class Ant:
def __init__(self, alpha, beta, num_nodes, edges):
self.alpha = alpha
self.beta = beta
self.num_nodes = num_nodes
self.edges = edges
self.tour = None
self.distance = 0.0
def _select_node(self):
roulette_wheel = 0.0
unvisited_nodes = [node for node in range(self.num_nodes) if node not in self.tour]
heuristic_total = 0.0
for unvisited_node in unvisited_nodes:
heuristic_total += self.edges[self.tour[-1]][unvisited_node].weight
for unvisited_node in unvisited_nodes:
roulette_wheel += pow(self.edges[self.tour[-1]][unvisited_node].pheromone, self.alpha) * \
pow((heuristic_total / self.edges[self.tour[-1]][unvisited_node].weight), self.beta)
random_value = random.uniform(0.0, roulette_wheel)
wheel_position = 0.0
for unvisited_node in unvisited_nodes:
wheel_position += pow(self.edges[self.tour[-1]][unvisited_node].pheromone, self.alpha) * \
pow((heuristic_total / self.edges[self.tour[-1]][unvisited_node].weight), self.beta)
if wheel_position >= random_value:
return unvisited_node
def find_tour(self):
self.tour = [random.randint(0, self.num_nodes - 1)]
while len(self.tour) < self.num_nodes:
self.tour.append(self._select_node())
return self.tour
def get_distance(self):
self.distance = 0.0
for i in range(self.num_nodes):
self.distance += self.edges[self.tour[i]][self.tour[(i + 1) % self.num_nodes]].weight
return self.distance
def __init__(self, mode='ACS', colony_size=10, elitist_weight=1.0, min_scaling_factor=0.001, alpha=1.0, beta=3.0,
rho=0.1, pheromone_deposit_weight=1.0, initial_pheromone=1.0, steps=100, nodes=None, labels=None):
self.mode = mode
self.colony_size = colony_size
self.elitist_weight = elitist_weight
self.min_scaling_factor = min_scaling_factor
self.rho = rho
self.pheromone_deposit_weight = pheromone_deposit_weight
self.steps = steps
self.num_nodes = len(nodes)
self.nodes = nodes
self.tours=[]
cities=self.nodes
if labels is not None:
self.labels = labels
else:
self.labels = range(1, self.num_nodes + 1)
self.edges = [[None] * self.num_nodes for _ in range(self.num_nodes)]
for i in range(self.num_nodes):
for j in range(i + 1, self.num_nodes):
self.edges[i][j] = self.edges[j][i] = self.Edge(i, j, math.sqrt(
pow(self.nodes[i][0] - self.nodes[j][0], 2.0) + pow(self.nodes[i][1] - self.nodes[j][1], 2.0)),
initial_pheromone)
self.ants = [self.Ant(alpha, beta, self.num_nodes, self.edges) for _ in range(self.colony_size)]
self.global_best_tour = None
self.global_best_tours =[]
self.global_best_distance = float("inf")
def _add_pheromone(self, tour, distance, weight=1.0):
pheromone_to_add = self.pheromone_deposit_weight / distance
for i in range(self.num_nodes):
self.edges[tour[i]][tour[(i + 1) % self.num_nodes]].pheromone += weight * pheromone_to_add
def _elitist(self):
path=[]
cost=[]
for step in range(self.steps):
self.tours=[]
for ant in self.ants:
self._add_pheromone(ant.find_tour(), ant.get_distance())
#self.tours.append(ant.tour)
l=[]
for i in ant.tour:
l.append(self.nodes[i])
self.tours.append(l)
if ant.distance < self.global_best_distance:
self.global_best_tour = ant.tour
self.global_best_distance = ant.distance
self._add_pheromone(self.global_best_tour, self.global_best_distance, weight=self.elitist_weight)
path.append(self.global_best_tour)
cost.append(self.global_best_distance)
#self.plottour(self.tours,self.global_best_tour)
#l=[]
#x=0
#for i in self.tours:
#l.append([])
#x=0;
#for j in i:
#l[x].append(self.nodes[j])
#x+=1
#plottour(l)
for i in range(self.num_nodes):
for j in range(i + 1, self.num_nodes):
self.edges[i][j].pheromone *= (1.0 - self.rho)
self.bhagwan_bharose(path,cost)
def run(self):
print('Started : {0}'.format(self.mode))
self._elitist()
print('Ended : {0}'.format(self.mode))
print('Sequence : <- {0} ->'.format(' - '.join(str(self.labels[i]) for i in self.global_best_tour)))
print('Total distance travelled to complete the tour : {0}\n'.format(round(self.global_best_distance, 2)))
def plot(self):
webbrowser.open_new_tab('a.html')
def bhagwan_bharose(self,paths, costs,animation_filename="ACO_TSP_Berlin52"):
global xx,yy
fig = plt.figure(figsize=(12, 8))
gs = fig.add_gridspec(2, 3, wspace=0.45, hspace=0.35)
# Plot berlin52 points
ax1 = fig.add_subplot(gs[0, 0])
ax1.scatter(xx,yy, c='r', edgecolors='black')
ax1.set_xlabel('x (kms)')
ax1.set_ylabel('y (kms)')
ax1.set_title('Cities', fontweight='bold', pad=10)
# Plot min cost found per iteration
ax2 = fig.add_subplot(gs[1, 0])
costline, = ax2.plot([], [])
ax2.set_xlabel('Iterations')
ax2.set_ylabel('Distance (kms)')
ax2.set_ylim(3000, 5500)
ax2.set_xlim(0, len(costs)+1)
ax2.set_title('Best path distance in every iteration', fontweight='bold', pad=10)
# Plot best path found per iteration
ax3 = fig.add_subplot(gs[:, 1:])
ax3.scatter(xx, yy, c='r', edgecolors='black')
ax3.set_xlabel('x (kms)')
ax3.set_ylabel('y (kms)')
title = ax3.set_title('Best Path Cost', fontweight='bold', fontsize=13, pad=10)
def init():
pass
def animate(iter):
if iter < len(costs):
costline.set_data(list(range(1, iter+2)), costs[:iter+1])
# print(costline)
if iter > 0 and iter <= len(costs):
ax3.lines.pop(0)
if iter < len(costs):
x = [xx[i] for i in paths[iter]]
y = [yy[j] for j in paths[iter]]
x.append(x[0])
y.append(y[0])
ax3.plot(x, y, '--', alpha=0.9)
title.set_text(f"Best Path Cost : {round(costs[iter], 2)} kms")
elif iter == len(costs):
x = [self.nodes[i][0] for i in self.global_best_tour]
y = [self.nodes[i][1] for i in self.global_best_tour]
x.append(x[0])
y.append(y[0])
ax3.plot(x, y, '-', c='#000000', alpha=0.8, lw=1.5)
# title.set_text(f"Best Path Cost : {round(aco.min_path_cost, 2)} kms")
return costline
anim = FuncAnimation(fig, animate, init_func=init, frames=len(costs)+1+50, interval=50, repeat=False)
anim.save('abc.gif', writer='')
print("Animation saved.")
ga()
_colony_size = 10
_steps = 50
cities=[]
elitist = SolveTSPUsingACO(mode='Elitist', colony_size=_colony_size, steps=_steps, nodes=_nodes)
elitist.run()
elitist.plot()
</div>
<script src="ace.js" type="text/javascript" charset="utf-8"></script>
<script>
var editor = ace.edit("editor");
editor.setTheme("ace/theme/twilight");
editor.session.setMode("ace/mode/javascript");
</script>
</body>
</html>