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Use dictionaries instead of index list in ant_colony

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Everything use cities tuples as keys.
This commit is contained in:
Johann Dreo 2014-03-18 11:51:05 +01:00
commit 26599368c4
1 changed files with 65 additions and 47 deletions
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108
ant_colony.py
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@ -4,6 +4,7 @@ import sys
import math import math
import random import random
from collections import Counter from collections import Counter
import path
def log( *args ): def log( *args ):
@ -23,40 +24,39 @@ def tour(lst):
yield (a,b) yield (a,b)
def euclidian_distance( ci, cj ): def euclidian_distance( ci, cj, graph = None):
return math.sqrt( float(ci[0] - cj[0])**2 + float(ci[1] - cj[1])**2 ) return math.sqrt( float(ci[0] - cj[0])**2 + float(ci[1] - cj[1])**2 )
def graph_distance( ci, cj, graph ):
p,c = path.astar( graph, ci, cj )
return c
def cost( permutation, cost_func, cities ): def cost( permutation, cost_func, cities ):
dist = 0 dist = 0
for i,j in tour(permutation): for ci,cj in tour(permutation):
dist += cost_func(cities[i],cities[j]) dist += cost_func( ci, cj, cities )
return dist return dist
def random_permutation( cities ): def initialize_pheromone_matrix( cities, init_value ):
# like random.shuffle(cities) but on a copy rows = {}
return sorted( cities, key=lambda i: random.random()) for i in cities:
cols = {}
for j in cities:
def initialize_pheromone_matrix( nb_cities, init_value ): cols[j] = init_value
rows = [] rows[i] = cols
for i in range(nb_cities):
cols = []
for j in range(nb_cities):
cols.append( init_value )
rows.append(cols)
return rows return rows
def choose( cities, last, exclude, pheromones, w_heuristic, w_history ): def choose( cities, last, exclude, pheromones, w_heuristic, w_history, cost_func = graph_distance ):
choices = [] choices = []
for i,city in enumerate(cities): for city in cities:
if i in exclude: if city in exclude:
continue continue
c = {"city" : i} c = {"city" : city}
c["history"] = pheromones[last][i] ** w_history c["history"] = pheromones[last][city] ** w_history
c["distance"] = euclidian_distance( cities[last], city ) c["distance"] = cost_func( last, city, cities )
c["heuristic"] = (1.0 / c["distance"]) ** w_heuristic c["heuristic"] = (1.0 / c["distance"]) ** w_heuristic
c["proba"] = c["history"] * c["heuristic"] c["proba"] = c["history"] * c["heuristic"]
choices.append(c) choices.append(c)
@ -64,7 +64,7 @@ def choose( cities, last, exclude, pheromones, w_heuristic, w_history ):
def proba_select( choices ): def proba_select( choices ):
s = sum( c["proba"] for c in choices ) s = float(sum( c["proba"] for c in choices ))
if s == 0.0: if s == 0.0:
return random.choice(choices)["city"] return random.choice(choices)["city"]
@ -82,14 +82,14 @@ def greedy_select( choices ):
return c["city"] return c["city"]
def walk( cities, pheromone, w_heuristic, c_greedy ): def walk( cities, pheromone, w_heuristic, w_history, c_greedy, cost_func = graph_distance, ):
assert( len(cities) > 0 ) assert( len(cities) > 0 )
# permutations are indices # permutations are indices
# randomly draw the first city index # randomly draw the first city index
permutation = [ random.randint(0,len(cities)-1) ] permutation = [ random.choice( cities.keys() ) ]
# then choose the next ones to build the permutation # then choose the next ones to build the permutation
while len(permutation) < len(cities): while len(permutation) < len(cities):
choices = choose( cities, permutation[-1], permutation, pheromone, w_heuristic, w_history = 1.0 ) choices = choose( cities, permutation[-1], permutation, pheromone, w_heuristic, w_history, cost_func )
do_greedy = ( random.random() <= c_greedy ) do_greedy = ( random.random() <= c_greedy )
if do_greedy: if do_greedy:
next_city = greedy_select( choices ) next_city = greedy_select( choices )
@ -116,19 +116,25 @@ def update_local( pheromones, candidate, w_pheromone, init_pheromone ):
pheromones[j][i] = value pheromones[j][i] = value
def search( max_iterations, nb_ants, decay, w_heuristic, w_pheromone, c_greedy, cities): def random_permutation( cities ):
best = { "permutation" : random_permutation(range(len(cities))) } # like random.shuffle(cities) but on a copy
best["cost"] = cost( best["permutation"], euclidian_distance, cities ) return sorted( cities, key=lambda i: random.random())
def search( max_iterations, nb_ants, decay, w_heuristic, w_pheromone, w_history, c_greedy, cities, cost_func = graph_distance ):
best = { "permutation" : random_permutation(cities) }
best["cost"] = cost( best["permutation"], cost_func, cities )
init_pheromone = 1.0 / float(len(cities)) * best["cost"] init_pheromone = 1.0 / float(len(cities)) * best["cost"]
pheromone = initialize_pheromone_matrix( len(cities), init_pheromone ) pheromone = initialize_pheromone_matrix( cities, init_pheromone )
for i in range(max_iterations): for i in range(max_iterations):
log( i ) log( i )
solutions = [] solutions = []
for j in range(nb_ants): for j in range(nb_ants):
log( "." ) log( "." )
candidate = {} candidate = {}
candidate["permutation"] = walk( cities, pheromone, w_heuristic, c_greedy ) candidate["permutation"] = walk( cities, pheromone, w_heuristic, w_history, c_greedy, cost_func )
candidate["cost"] = cost( candidate["permutation"], euclidian_distance, cities ) candidate["cost"] = cost( candidate["permutation"], cost_func, cities )
if candidate["cost"] < best["cost"]: if candidate["cost"] < best["cost"]:
best = candidate best = candidate
update_local( pheromone, candidate, w_pheromone, init_pheromone ) update_local( pheromone, candidate, w_pheromone, init_pheromone )
@ -142,21 +148,33 @@ if __name__ == "__main__":
max_it = 40 max_it = 40
num_ants = 10 num_ants = 10
decay = 0.1 decay = 0.1
c_heur = 2.5 w_heur = 2.5
c_local_phero = 0.1 w_local_phero = 0.1
c_greed = 0.9 c_greed = 0.9
w_history = 1.0
print "Berlin euclidian TSP" print """Graph TSP:
berlin52 = [[565,575],[25,185],[345,750],[945,685],[845,655], -1 0 2 : x
[880,660],[25,230],[525,1000],[580,1175],[650,1130],[1605,620], 1 o o-----o
[1220,580],[1465,200],[1530,5],[845,680],[725,370],[145,665], | | |
[415,635],[510,875],[560,365],[300,465],[520,585],[480,415], 0 o--o-----o
[835,625],[975,580],[1215,245],[1320,315],[1250,400],[660,180], | |
[410,250],[420,555],[575,665],[1150,1160],[700,580],[685,595], | |
[685,610],[770,610],[795,645],[720,635],[760,650],[475,960], -2 o--o-----o
[95,260],[875,920],[700,500],[555,815],[830,485],[1170,65], :
[830,610],[605,625],[595,360],[1340,725],[1740,245]] y
"""
G = {
( 0, 0) : [(-1, 0),( 0, 1),( 2, 0),( 0,-2)],
( 0, 1) : [( 0, 0),( 2, 1)],
( 0,-2) : [( 0, 0),( 2,-2),(-1,-2)],
(-1, 0) : [(-1, 1),( 0, 0)],
(-1, 1) : [(-1, 0)],
(-1,-2) : [( 0,-2)],
( 2, 0) : [( 2, 1),( 2,-2),( 0, 0)],
( 2, 1) : [( 0, 1),( 2, 0)],
( 2,-2) : [( 2, 0),( 0,-2)],
}
best = search( max_it, num_ants, decay, c_heur, c_local_phero, c_greed, berlin52 ) best = search( max_it, num_ants, decay, w_heur, w_local_phero, w_history, c_greed, G, cost_func = graph_distance )
print best["cost"], best["permutation"] print best["cost"], best["permutation"]