diff --git a/gui/tsp.py b/gui/tsp.py
index 6a460261e..1830cba23 100644
--- a/gui/tsp.py
+++ b/gui/tsp.py
@@ -1,8 +1,10 @@
from tkinter import *
+from tkinter import messagebox
import sys
import os.path
sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
from search import *
+import utils
import numpy as np
distances = {}
@@ -56,6 +58,7 @@ def __init__(self, root, all_cities):
self.calculate_canvas_size()
self.button_text = StringVar()
self.button_text.set("Start")
+ self.algo_var = StringVar()
self.all_cities = all_cities
self.frame_select_cities = Frame(self.root)
self.frame_select_cities.grid(row=1)
@@ -85,9 +88,18 @@ def create_buttons(self):
""" Create start and quit button """
Button(self.frame_select_cities, textvariable=self.button_text,
- command=self.run_traveling_salesman).grid(row=3, column=4, sticky=E + W)
- Button(self.frame_select_cities, text='Quit', command=self.root.destroy).grid(
- row=3, column=5, sticky=E + W)
+ command=self.run_traveling_salesman).grid(row=5, column=4, sticky=E + W)
+ Button(self.frame_select_cities, text='Quit', command=self.on_closing).grid(
+ row=5, column=5, sticky=E + W)
+
+ def create_dropdown_menu(self):
+ """ Create dropdown menu for algorithm selection """
+
+ choices = {'Simulated Annealing', 'Genetic Algorithm', 'Hill Climbing'}
+ self.algo_var.set('Simulated Annealing')
+ dropdown_menu = OptionMenu(self.frame_select_cities, self.algo_var, *choices)
+ dropdown_menu.grid(row=4, column=4, columnspan=2, sticky=E + W)
+ dropdown_menu.config(width=19)
def run_traveling_salesman(self):
""" Choose selected citites """
@@ -151,13 +163,30 @@ def create_canvas(self, problem):
variable=self.speed, label="Speed ----> ", showvalue=0, font="Times 11",
relief="sunken", cursor="gumby")
speed_scale.grid(row=1, columnspan=5, sticky=N + S + E + W)
- self.temperature = IntVar()
- temperature_scale = Scale(self.frame_canvas, from_=100, to=0, orient=HORIZONTAL,
+
+ if self.algo_var.get() == 'Simulated Annealing':
+ self.temperature = IntVar()
+ temperature_scale = Scale(self.frame_canvas, from_=100, to=0, orient=HORIZONTAL,
length=200, variable=self.temperature, label="Temperature ---->",
font="Times 11", relief="sunken", showvalue=0, cursor="gumby")
-
- temperature_scale.grid(row=1, column=5, columnspan=5, sticky=N + S + E + W)
- self.simulated_annealing_with_tunable_T(problem, map_canvas)
+ temperature_scale.grid(row=1, column=5, columnspan=5, sticky=N + S + E + W)
+ self.simulated_annealing_with_tunable_T(problem, map_canvas)
+ elif self.algo_var.get() == 'Genetic Algorithm':
+ self.mutation_rate = DoubleVar()
+ self.mutation_rate.set(0.05)
+ mutation_rate_scale = Scale(self.frame_canvas, from_=0, to=1, orient=HORIZONTAL,
+ length=200, variable=self.mutation_rate, label='Mutation Rate ---->',
+ font='Times 11', relief='sunken', showvalue=0, cursor='gumby', resolution=0.001)
+ mutation_rate_scale.grid(row=1, column=5, columnspan=5, sticky='nsew')
+ self.genetic_algorithm(problem, map_canvas)
+ elif self.algo_var.get() == 'Hill Climbing':
+ self.no_of_neighbors = IntVar()
+ self.no_of_neighbors.set(100)
+ no_of_neighbors_scale = Scale(self.frame_canvas, from_=10, to=1000, orient=HORIZONTAL,
+ length=200, variable=self.no_of_neighbors, label='Number of neighbors ---->',
+ font='Times 11',relief='sunken', showvalue=0, cursor='gumby')
+ no_of_neighbors_scale.grid(row=1, column=5, columnspan=5, sticky='nsew')
+ self.hill_climbing(problem, map_canvas)
def exp_schedule(k=100, lam=0.03, limit=1000):
""" One possible schedule function for simulated annealing """
@@ -191,6 +220,102 @@ def simulated_annealing_with_tunable_T(self, problem, map_canvas, schedule=exp_s
map_canvas.update()
map_canvas.after(self.speed.get())
+ def genetic_algorithm(self, problem, map_canvas):
+ """ Genetic Algorithm modified for the given problem """
+
+ def init_population(pop_number, gene_pool, state_length):
+ """ initialize population """
+
+ population = []
+ for i in range(pop_number):
+ population.append(utils.shuffled(gene_pool))
+ return population
+
+ def recombine(state_a, state_b):
+ """ recombine two problem states """
+
+ start = random.randint(0, len(state_a) - 1)
+ end = random.randint(start + 1, len(state_a))
+ new_state = state_a[start:end]
+ for city in state_b:
+ if city not in new_state:
+ new_state.append(city)
+ return new_state
+
+ def mutate(state, mutation_rate):
+ """ mutate problem states """
+
+ if random.uniform(0, 1) < mutation_rate:
+ sample = random.sample(range(len(state)), 2)
+ state[sample[0]], state[sample[1]] = state[sample[1]], state[sample[0]]
+ return state
+
+ def fitness_fn(state):
+ """ calculate fitness of a particular state """
+
+ fitness = problem.value(state)
+ return int((5600 + fitness) ** 2)
+
+ current = Node(problem.initial)
+ population = init_population(100, current.state, len(current.state))
+ all_time_best = current.state
+ while(1):
+ population = [mutate(recombine(*select(2, population, fitness_fn)), self.mutation_rate.get()) for i in range(len(population))]
+ current_best = utils.argmax(population, key=fitness_fn)
+ if fitness_fn(current_best) > fitness_fn(all_time_best):
+ all_time_best = current_best
+ self.cost.set("Cost = " + str('%0.3f' % (-1 * problem.value(all_time_best))))
+ map_canvas.delete('poly')
+ points = []
+ for city in current_best:
+ points.append(self.frame_locations[city][0])
+ points.append(self.frame_locations[city][1])
+ map_canvas.create_polygon(points, outline='red', width=1, fill='', tag='poly')
+ best_points = []
+ for city in all_time_best:
+ best_points.append(self.frame_locations[city][0])
+ best_points.append(self.frame_locations[city][1])
+ map_canvas.create_polygon(best_points, outline='red', width=3, fill='', tag='poly')
+ map_canvas.update()
+ map_canvas.after(self.speed.get())
+
+ def hill_climbing(self, problem, map_canvas):
+ """ hill climbing where number of neighbors is taken as user input """
+
+ def find_neighbors(state, number_of_neighbors=100):
+ """ finds neighbors using two_opt method """
+
+ neighbors = []
+ for i in range(number_of_neighbors):
+ new_state = problem.two_opt(state)
+ neighbors.append(Node(new_state))
+ state = new_state
+ return neighbors
+
+ current = Node(problem.initial)
+ while(1):
+ neighbors = find_neighbors(current.state, self.no_of_neighbors.get())
+ neighbor = utils.argmax_random_tie(neighbors, key=lambda node: problem.value(node.state))
+ map_canvas.delete('poly')
+ points = []
+ for city in current.state:
+ points.append(self.frame_locations[city][0])
+ points.append(self.frame_locations[city][1])
+ map_canvas.create_polygon(points, outline='red', width=3, fill='', tag='poly')
+ neighbor_points = []
+ for city in neighbor.state:
+ neighbor_points.append(self.frame_locations[city][0])
+ neighbor_points.append(self.frame_locations[city][1])
+ map_canvas.create_polygon(neighbor_points, outline='red', width=1, fill='', tag='poly')
+ map_canvas.update()
+ map_canvas.after(self.speed.get())
+ if problem.value(neighbor.state) > problem.value(current.state):
+ current.state = neighbor.state
+ self.cost.set("Cost = " + str('%0.3f' % (-1 * problem.value(current.state))))
+
+ def on_closing(self):
+ if messagebox.askokcancel('Quit', 'Do you want to quit?'):
+ self.root.destroy()
def main():
all_cities = []
@@ -212,6 +337,8 @@ def main():
cities_selection_panel = TSP_Gui(root, all_cities)
cities_selection_panel.create_checkboxes()
cities_selection_panel.create_buttons()
+ cities_selection_panel.create_dropdown_menu()
+ root.protocol('WM_DELETE_WINDOW', cities_selection_panel.on_closing)
root.mainloop()
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