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use the full-matrix pheromones model

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Johann Dreo 2014-03-22 17:07:21 +01:00
commit 3f556fe0e7
1 changed files with 61 additions and 24 deletions
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85
ant_colony.py
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@ -8,6 +8,7 @@ import path
def LOG( *args ):
"""Print something on stderr and flush"""
for msg in args:
sys.stderr.write( str(msg) )
sys.stderr.write(" ")
@ -15,6 +16,7 @@ def LOG( *args ):
def LOGN( *args ):
"""Print something on stdeer, with a trailing new line, and flush"""
LOG( *args )
LOG("\n")
@ -40,18 +42,9 @@ def cost( permutation, cost_func, cities ):
return dist
def initialize_pheromones( cities, init_value ):
rows = {}
for i in cities:
cols = {}
for j in cities:
cols[j] = init_value
rows[i] = cols
return rows
def choose( cities, last, exclude, pheromones, w_heuristic, w_history, cost_func = graph_distance ):
def look( cities, last, exclude, pheromones, w_heuristic, w_history, cost_func = graph_distance ):
choices = []
# gather informations about possible moves
for current in cities:
if current in exclude:
# This is faster than "if current not in exclude"
@ -65,7 +58,7 @@ def choose( cities, last, exclude, pheromones, w_heuristic, w_history, cost_func
return choices
def proba_select( choices ):
def proba_choose( choices ):
s = float(sum( c["proba"] for c in choices ))
if s == 0.0:
return random.choice(choices)["city"]
@ -79,24 +72,24 @@ def proba_select( choices ):
return c[-1]["city"]
def greedy_select( choices ):
def greedy_choose( choices ):
c = max( choices, key = lambda c : c["proba"] )
return c["city"]
def walk( cities, pheromone, w_heuristic, w_history, c_greedy, cost_func = graph_distance, ):
def walk( cities, pheromones, w_heuristic, w_history, c_greedy, cost_func = graph_distance ):
assert( len(cities) > 0 )
# permutations are indices
# randomly draw the first city index
permutation = [ random.choice( cities.keys() ) ]
# then choose the next ones to build the permutation
while len(permutation) < len(cities):
choices = choose( cities, permutation[-1], permutation, pheromone, w_heuristic, w_history, cost_func )
choices = look( cities, permutation[-1], permutation, pheromones, w_heuristic, w_history, cost_func )
do_greedy = ( random.random() <= c_greedy )
if do_greedy:
next_city = greedy_select( choices )
next_city = greedy_choose( choices )
else:
next_city = proba_select( choices )
next_city = proba_choose( choices )
permutation.append( next_city )
# assert no duplicates
@ -104,27 +97,71 @@ def walk( cities, pheromone, w_heuristic, w_history, c_greedy, cost_func = graph
return permutation
def update_global( pheromones, candidate, decay ):
def initialize_pheromones_whole( cities, init_value ):
rows = {}
for i in cities:
cols = {}
for j in cities:
cols[j] = init_value
rows[i] = cols
return rows
def update_global_whole( pheromones, candidate, graph, decay ):
for i,j in tour(candidate["permutation"]):
value = ((1.0 - decay) * pheromones[i][j]) + (decay * (1.0/candidate["cost"]))
pheromones[i][j] = value
pheromones[j][i] = value
def update_local( pheromones, candidate, w_pheromone, init_pheromone ):
def update_local_whole( pheromones, candidate, graph, w_pheromone, init_pheromone ):
for i,j in tour(candidate["permutation"]):
value = ((1.0 - w_pheromone) * pheromones[i][j]) + (w_pheromone * init_pheromone)
pheromones[i][j] = value
pheromones[j][i] = value
def initialize_pheromones_neighbors( cities, init_value ):
rows = {}
for i in cities:
cols = {}
for j in cities:
# set an init value for neighbors only
if j in cities[i]:
cols[j] = init_value
else: # else, there should be no edge
cols[j] = 0
rows[i] = cols
return rows
def update_global_neighbors( pheromones, candidate, graph, decay ):
for ci,cj in tour(candidate["permutation"]):
# subpath between ci and cj
p,c = path.astar( graph, ci, cj )
# deposit pheromones on each edges of the subpath
for i,j in zip(p,p[1:]):
value = ((1.0 - decay) * pheromones[i][j]) + (decay * (1.0/candidate["cost"]))
pheromones[i][j] = value
pheromones[j][i] = value
def update_local_neighbors( pheromones, candidate, graph, w_pheromone, init_pheromone ):
for ci,cj in tour(candidate["permutation"]):
p,c = path.astar( graph, ci, cj )
for i,j in zip(p,p[1:]):
value = ((1.0 - w_pheromone) * pheromones[i][j]) + (w_pheromone * init_pheromone)
pheromones[i][j] = value
pheromones[j][i] = value
def search( cities, max_iterations, nb_ants, decay, w_heuristic, w_pheromone, w_history, c_greedy, cost_func = graph_distance ):
# like random.shuffle(cities) but on a copy
best = { "permutation" : sorted( cities, key=lambda i: random.random()) }
best["cost"] = cost( best["permutation"], cost_func, cities )
init_pheromone = 1.0 / float(len(cities)) * best["cost"]
pheromone = initialize_pheromones( cities, init_pheromone )
pheromones = initialize_pheromones_whole( cities, init_pheromone )
for i in range(max_iterations):
LOG( i )
@ -132,15 +169,15 @@ def search( cities, max_iterations, nb_ants, decay, w_heuristic, w_pheromone, w_
for j in range(nb_ants):
LOG( "." )
candidate = {}
candidate["permutation"] = walk( cities, pheromone, w_heuristic, w_history, c_greedy, cost_func )
candidate["permutation"] = walk( cities, pheromones, w_heuristic, w_history, c_greedy, cost_func )
candidate["cost"] = cost( candidate["permutation"], cost_func, cities )
if candidate["cost"] < best["cost"]:
best = candidate
update_local( pheromone, candidate, w_pheromone, init_pheromone )
update_global( pheromone, best, decay )
update_local_whole( pheromones, candidate, cities, w_pheromone, init_pheromone )
update_global_whole( pheromones, best, cities, decay )
LOGN( best["cost"] )
return best,pheromone
return best,pheromones
if __name__ == "__main__":