def calcMMD(self, geneSerial, data):
customers = []
for item in data.CUSTOMERS:
tmp = Customer()
tmp.x = copy.deepcopy(item.x)
tmp.y = copy.deepcopy(item.y)
tmp.demand = copy.deepcopy(item.demand)
customers.append(tmp)
providers = []
sigmaCost = 0
sigmaCapacity = 0
sigmaDemand = 0
mmd = -1000.00
for i in range(0, len(geneSerial)):
tmpProvider = Provider()
tmpProvider.x = copy.deepcopy(data.PROVIDERS[i].x)
tmpProvider.y = copy.deepcopy(data.PROVIDERS[i].y)
tmpProvider.capacity = copy.deepcopy(
data.PROVIDERS[i].capacity[geneSerial[i]])
tmpProvider.cost = copy.deepcopy(
data.PROVIDERS[i].cost[geneSerial[i]])
sigmaCost = sigmaCost + tmpProvider.cost
sigmaCapacity = sigmaCapacity + tmpProvider.capacity
providers.append(tmpProvider)
for item in customers:
sigmaDemand = sigmaDemand + item.demand
if sigmaCapacity >= sigmaDemand:
swapchainsolver = SwapChainSolver(providers, customers)
mmd = swapchainsolver.Solver()
return mmd
def calcFitness(self, geneSerial, data, D):
"""
usage ChromosomeNumber,geneSerial,data,D
return fitness for this1 Chromosome
"""
alpha = self.m_Alpha
beta = self.m_Beta
# alpha and beta are weight factor
customers = []
fitness = 0
for item in data.CUSTOMERS:
tmp = Customer()
tmp.x = copy.deepcopy(item.x)
tmp.y = copy.deepcopy(item.y)
tmp.demand = copy.deepcopy(item.demand)
customers.append(tmp)
providers = []
sigmaCost = 0
sigmaCapacity = 0
sigmaDemand = 0
mmd = self.m_D * 1000.0
for i in range(0, len(geneSerial)):
tmpProvider = Provider()
tmpProvider.x = copy.deepcopy(data.PROVIDERS[i].x)
tmpProvider.y = copy.deepcopy(data.PROVIDERS[i].y)
tmpProvider.capacity = copy.deepcopy(
data.PROVIDERS[i].capacity[geneSerial[i]])
tmpProvider.cost = copy.deepcopy(
data.PROVIDERS[i].cost[geneSerial[i]])
sigmaCost = sigmaCost + tmpProvider.cost
sigmaCapacity = sigmaCapacity + tmpProvider.capacity
providers.append(tmpProvider)
for item in customers:
sigmaDemand = sigmaDemand + item.demand
if sigmaCapacity >= sigmaDemand:
swapchainsolver = SwapChainSolver(providers, customers)
mmd = swapchainsolver.Solver()
if mmd > D:
fitness = -4.0
else:
if sigmaCost != 0:
fitness = float(4.0 / sigmaCost)
else:
fitness = 8.0
else:
fitness = -8.0
# print("fitness,mmd,sigmaCapacity,sigmaCost,sigmaDemand:",fitness,mmd,sigmaCapacity,sigmaCost,sigmaDemand)
return math.exp(fitness), mmd, sigmaCapacity, sigmaCost, sigmaDemand