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acs_solver.py
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acs_solver.py
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#!/usr/bin/python3
import numpy as np
import random
import matplotlib.pyplot as plt
class Edge:
def __init__(self, start, end, length):
self.start = start
self.end = end
self.length = length
def __str__(self):
return str(self.start) + " -> " + str(self.end)
class Solution:
def __init__(self, path, pathSize):
self.path = path
self.pathSize = pathSize
""" Escolhe o incicio mais pesado """
def choose_start(startList):
total = 0
for i in range(len(startList)):
total += startList[i]
#total = sum(e.length for e in choices)
r = random.uniform(0, total)
upto = 0
for i in range(len(startList)):
if upto + startList[i] >= r:
return i
upto += startList[i]
assert False, "Erro start!!!"
return 0
"""
Escolhe a aresta mais pesada em forma de roleta.
"""
def weighted_choice(choices):
total = sum(e.length for e in choices)
r = random.uniform(0, total)
upto = 0
for e in choices:
if upto + e.length >= r:
return e
upto += e.length
assert False, "Erro weighted_choice!!!"
"""
Retorna a melhor escolha dentro das possiveis
"""
def maximalChoice(choices):
bestV = 0
bestE = 0
for e in choices:
if e.length >= bestV:
bestE = e
bestV = e.length
return bestE
""" Cria opções de escolha """
def createChoices(start, unvisited, distM, phoM, beta):
choices = []
for end in unvisited:
# Arestas de tamanho -1 não são consideradas
if (distM[start, end] > -1):
choices.append(Edge(start, end, (distM[start, end] ** beta) * phoM[start, end]))
return choices
""" Retorna melhor opção viavel da lista de candidatos """
def getFromCandidateList(clM, start, unvisited):
for option in clM[start,:]:
if option in unvisited:
return option
return -1
""" Efetua os passeios de cada formiga """
def antWalk(nodes, nodesSize, distM, phoM, start, phomDeposited, clM,
beta, q0, cl):
unvisited = list(range(0, nodesSize))
i = start
unvisited.remove(i)
visited = [i]
path = []
pathSize = 0
while len(unvisited) > 0:
j = step(i, unvisited, distM, phoM, beta, q0, clM, cl)
# Verifica se não há mais caminhos válidos
if (j < 0):
return None, -1
visited.append(j)
unvisited.remove(j)
localUpdateDecrease(i, j, phomDeposited, phoM)
path.append(Edge(i, j, distM[i, j]))
pathSize += distM[i, j]
i = j
return path, pathSize
""" Caminha para um vértice """
def step(start, unvisited, distM, phoM, beta, q0, clM, cl):
q = random.random()
if cl and q <= q0:
end = getFromCandidateList(clM, start, unvisited)
if end > 0:
return end
choices = createChoices(start, unvisited, distM, phoM, beta)
if len(choices) == 0:
return -1
if q <= q0:
return maximalChoice(choices).end
else:
return weighted_choice(choices).end
""" Adiciona os feromonios dada uma solução de entrada """
def buildSolutionByPath(initialPath, distM, phoM, startList, phomDeposited):
solution = Solution([], 0)
start = initialPath[0]
startList[start] += 500
for end in initialPath:
solution.path.append(Edge(start, end, distM[start, end]))
solution.pathSize += distM[start, end]
phoM[start, end] += phomDeposited * 500
start = end
return solution
""" Update global de execução """
def globalUpdate(bestSolution, alpha, phoM):
# Inicializa matriz de distancias
for index, v in np.ndenumerate(phoM):
phoM[index] = (1 - alpha) * phoM[index]
for edge in bestSolution.path:
phoM[edge.start,edge.end] += alpha * bestSolution.pathSize
""" Reduz a quantidade de feromonio de um aresta visitada """
def localUpdateDecrease(start, end, phomDeposited, phoM, alpha = 0.1):
phoM[start, end] = (1 - alpha) * phoM[start, end] + (alpha * phomDeposited )
""" Reduz a quantidade de feromonio de todas as areastas """
def localUpdate(path,pathSize, phomDeposited, alpha, phoM, bestPath):
for edge in path:
phoM[edge.start,edge.end] = (1 - alpha) * phoM[edge.start,edge.end]
+ (alpha * phomDeposited )
""" Inicializa a lista de candidatos """
def initializeCL(nodesSize, distM):
clM = np.zeros((nodesSize, min(15, nodesSize)))
for i in range(nodesSize):
sorted = np.argsort(distM[i,:])
clM[i,:] = sorted[::-1][:min(15, nodesSize)]
return clM.astype(int)
""" Optimzação local 3-opt para um caminho """
def localOptimization(path, clM, distM, nodesSize):
optV = list(range(0, nodesSize))
for i in range(1):
j = random.choice(optV)
e1 = path[j]
k = e1.start
l = e1.end
q = clM[k, 0]
if q != l and distM[k, l] < distM[k, q]:
for e in path:
if e.end == q:
e2 = e
p = e.start
break
optV.remove(k)
optV.remove(l)
optV.remove(q)
optV.remove(p)
r = random.choice(optV)
for e in path:
if e.start == r:
s = e.end
e3 = e
break
print(distM[k, q] + distM[p, s] + distM[r, l], distM[k, q] + distM[p, s] + distM[r, l])
if (distM[k, q] + distM[p, s] + distM[r, l]) > ([k, l] + distM[p, q] + distM[r, s]):
e1.end = q
e1.length = distM[k, q]
e2.end = s
e2.length = distM[p, s]
e3.end = l
e3.length = distM[r, l]
print("entrouuuu ")
"""
Executa colonia de formigas em busca do caminho hamiltoniano de maior peso
"""
def solve(nodes, dist, nAnts = 10, iterations = 150, initialPath = [],
alpha=0.1, beta = 2, q0 = 0.9, cl = True):
nodesSize = len(nodes)
# Reseta a seed do random
random.seed()
# Calcula o feromonio inicial
phomDeposited = 1500/(nodesSize)
distM = np.zeros((nodesSize, nodesSize))
phoM = np.zeros((nodesSize, nodesSize))
phoM[:] = phomDeposited
startList = [phomDeposited] * nodesSize
#startList.fill(0.01)
# Inicializa matriz de distancias
for index, v in np.ndenumerate(distM):
if index[0] != index[1]:
distM[index] = dist(nodes[index[0]], nodes[index[1]])
else:
distM[index] = 0
clM = initializeCL(nodesSize, distM)
if len(initialPath) > 0:
solution = buildSolutionByPath(initialPath, distM, phoM, startList,
phomDeposited)
else:
solution = Solution([], 1)
print("Inicialização", solution.pathSize)
plateu = 0
# Executa determinado numero de interações zerando os parametros
antPaths = [None]* nAnts
antPathsSize = [0] * nAnts
be = []
for j in range(iterations):
# phoM[:] = phomDeposited
# startList = [phomDeposited] * nodesSize
starts = random.sample(range(nodesSize), nAnts)
# Caminha com cada formiga pelo grafo
for i in range(0, nAnts):
path, pathSize = antWalk(nodes, nodesSize, distM, phoM, starts[i],
phomDeposited, clM, beta, q0, cl)
antPaths[i] = path
antPathsSize[i] = pathSize
# Atualiza os feromonios
for i in range(0, nAnts):
path = antPaths[i]
pathSize = antPathsSize[i]
if pathSize > 0:
# Calcula a pontuação de iniciação dos caminhos
startList[path[0].start] += phomDeposited * pathSize
# Calula a pontuação dos feromonios da iteração
# localUpdate(path, pathSize, phomDeposited, alpha, phoM, solution.pathSize)
if solution.pathSize == 0 or solution.pathSize < pathSize:
print("Novo ótimo:", pathSize)
plateu = 0
solution = Solution(path, pathSize)
else:
plateu = plateu + 1;
#localOptimization(path, clM, distM, nodesSize)
if plateu > iterations * nAnts /2:
break
# Atualiza os vetores do grafico
v = 0
for edge in solution.path:
v = v + (distM[edge.start, edge.end] ** beta) * phoM[edge.start, edge.end]
v = v/ nodesSize
be.append(v)
globalUpdate(solution, alpha, phoM)
# Plota o grafico da relação de feromonios.
# plt.figure(1)
# plt.plot(be, label = "be")
# plt.show()
#Converte indices para nos
for edge in solution.path:
edge.start = nodes[edge.start]
edge.end = nodes[edge.end]
print("Final", solution.pathSize, " plato ", plateu, "\n")
return solution