def tabu2(arr_edge,all_edge,nodes,tabu_list): solution_array = [] free_edges = get_free_edges(arr_edge,all_edge) arr_edge_cost = util.blabla(nodes,arr_edge) #solution_array.append((arr_edge,arr_edge_cost)) for e in arr_edge: temp = [] for k in arr_edge: if(k.same(e)): pass else: temp.append(k.copy()) nodes1 = [] nodes2 = [] connected_to(e.v1,temp,nodes1) connected_to(e.v2,temp,nodes2) c_edges = cut_edges(nodes1,nodes2,free_edges) for c_edge in c_edges: temp.append(c_edge) cost = util.blabla(nodes,temp) a,b = util.copy_array(temp,cost) solution_array.append((a,b,c_edge.copy())) temp.remove(c_edge) t_arr,mincost,e_min = solution_array[0] minpos = 0 i=0 for a,b,e in solution_array[1:]: i+=1 if(b<mincost): minpos = i mincost = b res_arr,res_cost,e_min = solution_array[minpos] tabu_list.append(e_min) return((res_arr,res_cost))
def heu_cut(arr_edge,all_edge,nodes): solution_array = [] free_edges = get_free_edges(arr_edge,all_edge) arr_edge_cost = util.blabla(nodes,arr_edge) #solution_array.append((arr_edge,arr_edge_cost)) for e in arr_edge: temp = [] for k in arr_edge: if(k.same(e)): pass else: temp.append(k.copy()) nodes1 = [] nodes2 = [] connected_to(e.v1,temp,nodes1) connected_to(e.v2,temp,nodes2) c_edges = cut_edges(nodes1,nodes2,free_edges) for c_edge in c_edges: temp.append(c_edge) cost = util.blabla(nodes,temp) solution_array.append(util.copy_array(temp,cost)) temp.remove(c_edge) t_arr,mincost = solution_array[0] minpos = 0 i=0 for a,b in solution_array[1:]: i+=1 if(b<mincost): minpos = i mincost = b
def crossover(pop,nodes,all_edge):
res = []
pop.sort(key = get_solution_cost)
res.append(pop[0])
t1,t2 = res[0]
for a,c in pop[1:]:
if(util.same_array(a,t1)):
pass
else:
res.append((a,c))
break
for i in range(POPULATION-2):
t1,t2 = fitness.roulette_wheel(pop)
p1,c = t1
p2,c = t2
arr_temp = pr.parent_reproduction(p1,p2,nodes)
counter =0
for a,b in res:
if(diffs(a,arr_temp)<2):
#print("mutation")
if(random.uniform(0,1)>0.5):
arr_temp = mutate(p1,all_edge,nodes)
else:
arr_temp = mutate(p2,all_edge,nodes)
cost = util.blabla(nodes,arr_temp)
res.append((arr_temp,cost))
return res
def generate_population(arr_edge,all_edge,nodes): tabu_list = [] solution_array = [] startcost = util.blabla(nodes,arr_edge) #solution_array.append((arr_edge,startcost)) temp_list = arr_edge for i in range(TABU_STEP): temp_list,cost = tabu.tabu2(temp_list,all_edge,nodes,tabu_list) solution_array.append((temp_list,cost)) return solution_array
def main():
nodes,temp = rgt.read("../graphs/graph_2.txt")
all_edge = []
for t in temp:
c,a,b = t.split(" ")
e = Edge.Edge(a,b,c)
all_edge.append(e)
list_of_list_of_list =spt.loop_root_SPT(nodes,temp)
trees = []
for list_of_list in list_of_list_of_list:
temp_tree = []
for lis in list_of_list:
for ed in lis:
if(util.edge_in_list(ed,temp_tree)):
pass
else:
temp_tree.append(ed)
trees.append(temp_tree)
mincost = util.blabla(nodes,trees[0])
minpos = 0
i =0
for t in trees[1:]:
i+=1
c_temp = util.blabla(nodes,t)
if(c_temp < mincost):
mincost = c_temp
minpos = i
print("pre eu cost = "+str(mincost))
a = trees[minpos]
mincost = util.blabla(nodes,a)
for i in range(10):
a,b = heu_cut(a,all_edge,nodes)
print("cost = "+str(b))
if(b<mincost):
mincost = b
elif(b == mincost):
break
util.print_array(a)
def first_population(nodes,temp): list_of_list_of_list =spt.loop_root_SPT(nodes,temp) trees = [] for list_of_list in list_of_list_of_list: temp_tree = [] for lis in list_of_list: for ed in lis: if(util.edge_in_list(ed,temp_tree)): pass else: temp_tree.append(ed) cost = util.blabla(nodes,temp_tree) trees.append((temp_tree,cost)) return trees
def grasp(nodes, edges):
start = timeit.default_timer()
lll = spt.loop_root_SPT(nodes, edges)
stop = timeit.default_timer()
print("Time for Dijkstra tree generator: " + str(stop - start))
trees = []
grasp_list = []
for ll in lll:
temp_tree = []
for lis in ll:
for ed in lis:
if (di.edge_in_list(ed, temp_tree)):
pass
else:
temp_tree.append(ed)
trees.append(temp_tree)
trees.sort(key=lambda x: cost_tree_grasp(nodes, x))
mincost = util.blabla(nodes, trees[0])
res = trees[random.randint(0, 2)]
grasp_cost_res = util.blabla(nodes, res)
return res, grasp_cost_res, mincost
def main():
file_name = "../graphs/graph_ppt.txt"
nodes,temp = util.read(file_name)
all_edge = []
for t in temp:
c,a,b = t.split(" ")
all_edge.append(Edge.Edge(a,b,c))
all_edge.sort(key = lambda x: x.cost)
arr_edge = []
'''for e in all_edge:
arr_edge.append(e)
a,b = util.cic(arr_edge)
if(a):
arr_edge.pop()'''
arr_edge = gk.kruskal(all_edge)
mincost = util.blabla(nodes,arr_edge)
minarr = arr_edge
print("pre tabu cost "+str(mincost))
newcost = mincost
tabu_list = []
counter = 0
tot = 0
start = timeit.default_timer()
for i in range(30):
newarr,newcost = tabu.tabu(arr_edge,all_edge,nodes,tabu_list)
print("step n "+str(i+1)+" cost = "+str(newcost))
stop = timeit.default_timer()
tot += stop-start
print("time = "+str(tot))
start = timeit.default_timer()
arr_edge = newarr
if(newcost >= mincost):
#util.print_array(tabu_list)
counter+=1
else:
minarr,mincost = util.copy_array(newarr,newcost)
counter =0
if(counter == 5):
break
print("best cost = "+str(mincost))
print("time = "+str(tot))
stop = timeit.default_timer()
tot += stop-start
util.print_array(minarr)
def tabu(arr_edge,all_edge,nodes,tabu_list): free_edge = [] solution_array = [] for e_all in all_edge: find = False for edge in arr_edge: if(edge.same(e_all)): find = True break if(not find): e = e_all.copy() free_edge.append(e) for e in free_edge: arr_edge.append(e) a,b = util.cic(arr_edge) for h in b: if(h.same(e)): continue elif(in_list(h,tabu_list)): continue else: util.pop_element(arr_edge,h) newmin = util.blabla(nodes,arr_edge) x,y = util.copy_array(arr_edge,newmin) solution_array.append((x,y,h.copy())) arr_edge.append(h) util.pop_element(arr_edge,e) minarr,mincost,tabuedge = solution_array[0] #return((minarr,mincost)) counter =0 minpos =0 for a,c,e in solution_array[1:]: counter +=1 if(c < mincost): minpos = counter mincost = c minarr,mincost,tabuedge = solution_array[minpos] append_to_tabu(tabuedge,tabu_list) return((minarr,mincost))
def main():
file_name = "../graphs/graph_ppt.txt"
nodes,temp = rgt.read(file_name)
all_edge = []
for t in temp:
c,a,b = t.split(" ")
all_edge.append(Edge.Edge(a,b,c))
#all_edge.sort(key = lambda x: x.cost)
arr_edge = []
arr_edge = gk.kruskal(all_edge)
#arr_edge = gk.grasp(all_edge)
mincost = util.blabla(nodes,arr_edge)
print("mincost first solution = "+str(mincost))
print("first solution")
util.print_array(arr_edge)
print("heuristic")
newcost= mincost
tot = 0
final_res,t = util.copy_array(arr_edge,mincost)
start = timeit.default_timer()
for i in range(50):
print(str(i+1)+"/50",end="\r")
arr_edge,newcost= heuristic(arr_edge,newcost,all_edge,nodes)
print("step "+str(i+1)+" solution to optimize cost = "+str(newcost))
stop = timeit.default_timer()
tot += stop-start
print("time = "+str(tot))
start = timeit.default_timer()
if(mincost> newcost):
mincost = newcost
final_res = arr_edge
elif(mincost == newcost):
break
print("result = "+str(mincost))
util.print_array(arr_edge)
'''plot_all_edge = []
def heuristic(arr_edge,base_cost,all_edge,nodes): solution_array = [] free_edge =[] for e_all in all_edge: find = False for edge in arr_edge: if(edge.same(e_all)): find = True break if(not find): e = e_all.copy() free_edge.append(e) for e in free_edge: arr_edge.append(e) a,b = util.cic(arr_edge) for h in b: if(h.same(e)): continue else: util.pop_element(arr_edge,h) newmin = util.blabla(nodes,arr_edge) solution_array.append(util.copy_array(arr_edge,newmin)) arr_edge.append(h) util.pop_element(arr_edge,e) solution_array.append((arr_edge,base_cost)) z,mincost = solution_array[0] minpos = 0 i=0 for a,c in solution_array: i+=1 if(mincost > c): minpos = i z,mincost = a,c return((z,mincost))
def cost_tree_grasp(nodes, tree):
cost = util.blabla(nodes, tree)
return cost