def EA_Main(locator, building_names, extraCosts, extraCO2, extraPrim, solarFeat, ntwFeat, gv, genCP=0):
"""
Evolutionary algorithm to optimize the district energy system's design
Parameters
----------
locator : string
paths to folders
finances / CO2 / Prim : float
costs [CHF] / emissions [kg CO2-eq] / primary energy needs [MJ oil]
previously calculated
solarFeat : class solarFeatures
includes data from solar files
ntwFeat : class ntwFeatures
includes data from the ntw optimization
genCP : int
generation to start the EA from (eg if there was a crash of the code)
Returns
-------
"""
t0 = time.clock()
# get number of buildings
nBuildings = len(building_names)
# set-up toolbox of DEAp library in python (containing the evolutionary algotirhtm
creator, toolbox = calc_ea_setup(nBuildings, gv)
# define objective function and register into toolbox
def evalConfig(ind):
(costs, CO2, prim) = eI.evalInd(
ind, building_names, locator, extraCosts, extraCO2, extraPrim, solarFeat, ntwFeat, gv
)
return (costs, CO2, prim)
toolbox.register("evaluate", evalConfig)
ntwList = ["1" * nBuildings]
epsInd = []
invalid_ind = []
# Evolutionary strategy
if genCP is 0:
# create population
pop = toolbox.population(n=gv.initialInd)
# Check network
for ind in pop:
eI.checkNtw(ind, ntwList, locator, gv)
# Evaluate the initial population
print "Evaluate initial population"
fitnesses = map(toolbox.evaluate, pop)
for ind, fit in zip(pop, fitnesses):
ind.fitness.values = fit
print ind.fitness.values, "fit"
# Save initial population
print "Save Initial population \n"
os.chdir(locator.pathMasterRes)
with open("CheckPointInitial", "wb") as CPwrite:
CPpickle = Pickler(CPwrite)
cp = dict(population=pop, generation=0, networkList=ntwList, epsIndicator=[], testedPop=[])
CPpickle.dump(cp)
else:
print "Recover from CP " + str(genCP) + "\n"
os.chdir(locator.pathMasterRes)
with open("CheckPoint" + str(genCP), "rb") as CPread:
CPunpick = Unpickler(CPread)
cp = CPunpick.load()
pop = cp["population"]
ntwList = cp["networkList"]
epsInd = cp["epsIndicator"]
PROBA, SIGMAP = gv.PROBA, gv.SIGMAP
# Evolution starts !
g = genCP
stopCrit = False # Threshold for the Epsilon indictor, Not used
while g < gv.NGEN and not stopCrit and (time.clock() - t0) < gv.maxTime:
g += 1
print "Generation", g
offspring = list(pop)
# Apply crossover and mutation on the pop
print "CrossOver"
for ind1, ind2 in zip(pop[::2], pop[1::2]):
child1, child2 = cx.cxUniform(ind1, ind2, PROBA, gv)
offspring += [child1, child2]
# First half of the EA: create new un-collerated configurations
if g < gv.NGEN / 2:
for mutant in pop:
print "Mutation Flip"
offspring.append(mut.mutFlip(mutant, PROBA, gv))
print "Mutation Shuffle"
offspring.append(mut.mutShuffle(mutant, PROBA, gv))
print "Mutation GU \n"
offspring.append(mut.mutGU(mutant, PROBA, gv))
# Third quarter of the EA: keep the good individuals but modify the shares uniformly
elif g < gv.NGEN * 3 / 4:
for mutant in pop:
print "Mutation Uniform"
offspring.append(mut.mutUniformCap(mutant, gv))
# Last quarter: keep the very good individuals and modify the shares with Gauss distribution
else:
for mutant in pop:
print "Mutation Gauss"
offspring.append(mut.mutGaussCap(mutant, SIGMAP, gv))
# Evaluate the individuals with an invalid fitness
# NB: every generation leads to the reevaluation of 4n / 2n / 2n individuals
# (n being the number of individuals in the previous generation)
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
print "Update Network list \n"
for ind in invalid_ind:
eI.checkNtw(ind, ntwList, locator, gv)
print "Re-evaluate the population"
fitnesses = map(toolbox.evaluate, invalid_ind)
print "......................................."
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
print ind.fitness.values, "new fit"
print "....................................... \n"
# Select the Pareto Optimal individuals
print "Pareto Selection"
selection = sel.selectPareto(offspring)
# Compute the epsilon criteria [and check the stopping criteria]
epsInd.append(eI.epsIndicator(pop, selection))
# if len(epsInd) >1:
# eta = (epsInd[-1] - epsInd[-2]) / epsInd[-2]
# if eta < gv.epsMargin:
# stopCrit = True
# The population is entirely replaced by the best individuals
print "Replace the population \n"
pop[:] = selection
print "....................................... \n GENERATION ", g
for ind in pop:
print ind.fitness.values, "selected fit"
print "....................................... \n"
# Create Checkpoint if necessary
if g % gv.fCheckPoint == 0:
os.chdir(locator.pathMasterRes)
print "Create CheckPoint", g, "\n"
with open("CheckPoint" + str(g), "wb") as CPwrite:
CPpickle = Pickler(CPwrite)
cp = dict(population=pop, generation=g, networkList=ntwList, epsIndicator=epsInd, testedPop=invalid_ind)
CPpickle.dump(cp)
if g == gv.NGEN:
print "Final Generation reached"
else:
print "Stopping criteria reached"
# Saving the final results
print "Save final results. " + str(len(pop)) + " individuals in final population"
print "Epsilon indicator", epsInd, "\n"
os.chdir(locator.pathMasterRes)
with open("CheckPointFinal", "wb") as CPwrite:
CPpickle = Pickler(CPwrite)
cp = dict(population=pop, generation=g, networkList=ntwList, epsIndicator=epsInd, testedPop=invalid_ind)
CPpickle.dump(cp)
print "Master Work Complete \n"
return pop, epsInd
def normalize_epsIndicator(pathX, generation):
"""
Calculates the normalized epsilon indicator
For all generations, the populations are normalized with regards to the
min / max over ALL generations
Parameters
----------
pathMasterRes : string
path to folder where checkpoints are stored
generation : int
generation up to which data are extracted
pathNtwRes : string
path to ntw result folder
Returns
-------
epsAll : list
normalized epsilon indicator from the beginning of the EA to generation
"""
epsAll = []
allPop = []
allPopNorm = []
# Load the populations
i = 1
while i < generation+1:
pop, eps, testedPop = sFn.readCheckPoint(pathX, i, storeData = 0)
i+=1
allPop.append(pop)
# Find the max and min
maxCosts =0
minCosts =0
maxCO2 =0
minCO2 =0
maxPrim =0
minPrim =0
for i in range(generation):
popScaned = allPop[i]
for ind in popScaned:
if ind.fitness.values[0] < minCosts:
minCosts = ind.fitness.values[0]
if ind.fitness.values[0] > maxCosts:
maxCosts = ind.fitness.values[0]
if ind.fitness.values[1] < minCO2:
minCO2 = ind.fitness.values[1]
if ind.fitness.values[1] > maxCO2:
maxCO2 = ind.fitness.values[1]
if ind.fitness.values[2] < minPrim:
minPrim = ind.fitness.values[2]
if ind.fitness.values[2] > maxPrim:
maxPrim = ind.fitness.values[2]
# Normalize
for k in range(generation):
popScaned = allPop[k]
popNorm = toolbox.clone(popScaned)
for i in range( len( popScaned ) ):
ind = popScaned[i]
NormCost = (ind.fitness.values[0] - minCosts) / (maxCosts - minCosts)
NormCO2 = (ind.fitness.values[1] - minCO2) / (maxCO2 - minCO2)
NormPrim = (ind.fitness.values[2] - minPrim) / (maxPrim - minPrim)
popNorm[i].fitness.values = (NormCost, NormCO2, NormPrim)
allPopNorm.append(popNorm)
# Compute the eps indicator
for i in range(generation-1):
frontOld = allPopNorm[i]
frontNew = allPopNorm[i+1]
epsAll.append(eI.epsIndicator(frontOld, frontNew))
return epsAll
def EA_Main(locator,
building_names,
extraCosts,
extraCO2,
extraPrim,
solarFeat,
ntwFeat,
gv,
genCP=0):
"""
Evolutionary algorithm to optimize the district energy system's design
Parameters
----------
locator : string
paths to folders
finances / CO2 / Prim : float
costs [CHF] / emissions [kg CO2-eq] / primary energy needs [MJ oil]
previously calculated
solarFeat : class solarFeatures
includes data from solar files
ntwFeat : class ntwFeatures
includes data from the ntw optimization
genCP : int
generation to start the EA from (eg if there was a crash of the code)
Returns
-------
"""
t0 = time.clock()
# get number of buildings
nBuildings = len(building_names)
# set-up toolbox of DEAp library in python (containing the evolutionary algotirhtm
creator, toolbox = calc_ea_setup(nBuildings, gv)
# define objective function and register into toolbox
def evalConfig(ind):
(costs, CO2, prim) = eI.evalInd(ind, building_names, locator,
extraCosts, extraCO2, extraPrim,
solarFeat, ntwFeat, gv)
return (costs, CO2, prim)
toolbox.register("evaluate", evalConfig)
ntwList = ["1" * nBuildings]
epsInd = []
invalid_ind = []
# Evolutionary strategy
if genCP is 0:
# create population
pop = toolbox.population(n=gv.initialInd)
# Check network
for ind in pop:
eI.checkNtw(ind, ntwList, locator, gv)
# Evaluate the initial population
print "Evaluate initial population"
fitnesses = map(toolbox.evaluate, pop)
for ind, fit in zip(pop, fitnesses):
ind.fitness.values = fit
print ind.fitness.values, "fit"
# Save initial population
print "Save Initial population \n"
os.chdir(locator.pathMasterRes)
with open("CheckPointInitial", "wb") as CPwrite:
CPpickle = Pickler(CPwrite)
cp = dict(population=pop,
generation=0,
networkList=ntwList,
epsIndicator=[],
testedPop=[])
CPpickle.dump(cp)
else:
print "Recover from CP " + str(genCP) + "\n"
os.chdir(locator.pathMasterRes)
with open("CheckPoint" + str(genCP), "rb") as CPread:
CPunpick = Unpickler(CPread)
cp = CPunpick.load()
pop = cp["population"]
ntwList = cp["networkList"]
epsInd = cp["epsIndicator"]
PROBA, SIGMAP = gv.PROBA, gv.SIGMAP
# Evolution starts !
g = genCP
stopCrit = False # Threshold for the Epsilon indictor, Not used
while g < gv.NGEN and not stopCrit and (time.clock() - t0) < gv.maxTime:
g += 1
print "Generation", g
offspring = list(pop)
# Apply crossover and mutation on the pop
print "CrossOver"
for ind1, ind2 in zip(pop[::2], pop[1::2]):
child1, child2 = cx.cxUniform(ind1, ind2, PROBA, gv)
offspring += [child1, child2]
# First half of the EA: create new un-collerated configurations
if g < gv.NGEN / 2:
for mutant in pop:
print "Mutation Flip"
offspring.append(mut.mutFlip(mutant, PROBA, gv))
print "Mutation Shuffle"
offspring.append(mut.mutShuffle(mutant, PROBA, gv))
print "Mutation GU \n"
offspring.append(mut.mutGU(mutant, PROBA, gv))
# Third quarter of the EA: keep the good individuals but modify the shares uniformly
elif g < gv.NGEN * 3 / 4:
for mutant in pop:
print "Mutation Uniform"
offspring.append(mut.mutUniformCap(mutant, gv))
# Last quarter: keep the very good individuals and modify the shares with Gauss distribution
else:
for mutant in pop:
print "Mutation Gauss"
offspring.append(mut.mutGaussCap(mutant, SIGMAP, gv))
# Evaluate the individuals with an invalid fitness
# NB: every generation leads to the reevaluation of 4n / 2n / 2n individuals
# (n being the number of individuals in the previous generation)
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
print "Update Network list \n"
for ind in invalid_ind:
eI.checkNtw(ind, ntwList, locator, gv)
print "Re-evaluate the population"
fitnesses = map(toolbox.evaluate, invalid_ind)
print "......................................."
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
print ind.fitness.values, "new fit"
print "....................................... \n"
# Select the Pareto Optimal individuals
print "Pareto Selection"
selection = sel.selectPareto(offspring)
# Compute the epsilon criteria [and check the stopping criteria]
epsInd.append(eI.epsIndicator(pop, selection))
#if len(epsInd) >1:
# eta = (epsInd[-1] - epsInd[-2]) / epsInd[-2]
# if eta < gv.epsMargin:
# stopCrit = True
# The population is entirely replaced by the best individuals
print "Replace the population \n"
pop[:] = selection
print "....................................... \n GENERATION ", g
for ind in pop:
print ind.fitness.values, "selected fit"
print "....................................... \n"
# Create Checkpoint if necessary
if g % gv.fCheckPoint == 0:
os.chdir(locator.pathMasterRes)
print "Create CheckPoint", g, "\n"
with open("CheckPoint" + str(g), "wb") as CPwrite:
CPpickle = Pickler(CPwrite)
cp = dict(population=pop,
generation=g,
networkList=ntwList,
epsIndicator=epsInd,
testedPop=invalid_ind)
CPpickle.dump(cp)
if g == gv.NGEN:
print "Final Generation reached"
else:
print "Stopping criteria reached"
# Saving the final results
print "Save final results. " + str(
len(pop)) + " individuals in final population"
print "Epsilon indicator", epsInd, "\n"
os.chdir(locator.pathMasterRes)
with open("CheckPointFinal", "wb") as CPwrite:
CPpickle = Pickler(CPwrite)
cp = dict(population=pop,
generation=g,
networkList=ntwList,
epsIndicator=epsInd,
testedPop=invalid_ind)
CPpickle.dump(cp)
print "Master Work Complete \n"
return pop, epsInd
