def __init__(self, capacity, items, duplicates=False):
Benchmark.__init__(self, len(items))
self.capacity = capacity
self.items = items
self.components = [swarm.TrailComponent((item[0]), value=item[1]) for item in items]
self.duplicates = duplicates
self.bias = 0.5
if self.duplicates:
max_count = [self.capacity // item[0] for item in self.items]
self.bounder = ec.DiscreteBounder([i for i in range(max(max_count)+1)])
else:
self.bounder = ec.DiscreteBounder([0, 1])
self.maximize = True
self._use_ants = False
def __init__(self, seed):
self.seed = seed # seed for random generator
# self.bounder = ec.Bounder(0, 1) # Discrete bounder to boolean values
self.bounder = ec.DiscreteBounder(
[0, 1]) # Discrete bounder to boolean values
self.maximize = False # Flag to define the problem nature
self.genCount = 0 # generation count
def __init__(self, benchmark, dimension_bits):
Benchmark.__init__(self, benchmark.dimensions, benchmark.objectives)
self.benchmark = benchmark
self.dimension_bits = dimension_bits
self.bounder = ec.DiscreteBounder([0, 1])
self.maximize = self.benchmark.maximize
self.__class__.__name__ = self.__class__.__name__ + ' ' + self.benchmark.__class__.__name__
def __init__(self, weights):
Benchmark.__init__(self, len(weights))
self.weights = weights
self.components = [swarm.TrailComponent((i, j), value=(1 / weights[i][j])) for i, j in itertools.permutations(range(len(weights)), 2)]
self.bias = 0.5
self.bounder = ec.DiscreteBounder([i for i in range(len(weights))])
self.maximize = True
self._use_ants = False
def __init__(self,
number_of_sentences,
coverage_matrix,
sentences_similarity,
ratio,
objectives=1):
self.dimensions = int(ratio * number_of_sentences)
self.objectives = objectives
self.bounder = None
self.maximize = True
self.coverage_matrix = coverage_matrix
self.sentences_similarity = sentences_similarity
self.ratio = ratio
self.number_of_sentences = number_of_sentences
self.bounder = ec.DiscreteBounder(
[i for i in range(number_of_sentences)])
def __init__(self, dimensions=43278):
# Column numbers of the Main_data_file: These are [the land type of various cell in 5 different scenarios: A-E
A_LU=3
B_LU=4
C_LU=5
D_LU=6
E_LU=7
cSUIT=11
cNoChange=9 # It is Whether the land allocation changed w.r.t the base case: 0: implies change
minA = {} # Minimum area for each land type (1-25)
maxA = {} # Max area for each land type
suit=[]
olt = {}
olt2nlt = {} # Original land type to number : Basically key value, key is the land type, value is id allotted to it
adj_List = {} # Adjacency list for each cell
dim=0
area = {}
sumIndArea = [] # Sum of area for each type of land type (not cell), total 25
indxMap = {}
areaMap = {} # area for each cell
landType = []
generator_count = 0
for i in range(50000):
suit.append(0)
#olt.append(0)
'''
0: AIR: Airport, 1: CIV: Civic, 2:POS: Preserved Open Space
3:WF, Working Farm
3:RL, Rural Living
3:RCR, Rural Crossroads
4:ER, Estate Residential
4:LLRN, Large Lot Residential Neighborhood
5:SLRN, Small Lot Residential Neighborhood
5:MFRN, Multifamily Residential Neighborhood
5:MRN, Mixed Residential Neighborhood
5:MHP
6:HRR, Healthcare
7:NCC, Neighborhood Commercial Center
7:SCC, Suburban Commercial Corridor
7:SOC, Suburban Office Center
8:LIC, Light Industrial
8:HIC, Heavy Industrial
8:HCC, Highway Commercial Corridor
9:MUN, Mixed-Use Neighborhood
9:TC, Town Center
9:UN, Urban Neighborhood
9:TOD, Transit Oriented Development
9:MUC, Mixed Use Center
9:MC, Metropolitan Center
9:UC, Urban Center
'''
olt2nlt["AIR"] = 1
olt2nlt["CIV"] = 2
olt2nlt["HIC"] = 3
olt2nlt["LIC"] = olt2nlt["HCC"] = 4
olt2nlt["LLRN"] = olt2nlt["ER"] = 5
olt2nlt["MFRN"] = 6
olt2nlt["MHP"] = 7
olt2nlt["MRN"] = 8
olt2nlt["MUN"] = 9
olt2nlt["NCC"] = 10
olt2nlt["POS"]= 11
olt2nlt["RCR"]= 12
olt2nlt["RL"] = 13
olt2nlt["SCC"]= 14
olt2nlt["SLRN"] = 15
olt2nlt["SOC"] = 16
olt2nlt["TC"] = 17
olt2nlt["UC"] = 18
olt2nlt["UN"] = 19
olt2nlt["VC"] = olt2nlt["HRR"] = 20
olt2nlt["WF"] = 21
olt2nlt["MUC"] = 22
olt2nlt["MC"] = 23
olt2nlt["TOD"] = 24
#Loading adj list for each cell
f= open("Adj_List_file.gal","r")
data = f.read().splitlines()[1:]
for line in data:
l = line.split(' ')
if not l[0] is '':
k = int(l[0])
for i in range(len(l)):
#print i, k
j = int(l[i])
if (i==0):
# Adding the key, i.e. the cell for which adj_list is being obtained, adds for the first time, when it does not exist int he dictionary
if not k in adj_List:
adj_List[k]=[]
else:
adj_List[k].append(j)
if not j in adj_List:
adj_List[j] =[]
if not k in adj_List[j]:
adj_List[j].append(k)
#print adj_List[20][1]
#Loading areas, landtype for each cell
g=open("Main_data_file.csv","r")
# initiating the total sums for each landtype to zero
for i in range(0,25):
sumIndArea.append(0)
datag = g.readlines()[1:]
for line in datag:
line = line.split('\t')
pt = int(line[0])
ar = float(line[2]) #area
tee = line[1] # landtype
temp_lt = olt2nlt[tee]
landType.append(temp_lt)
# indxMap : contains decision (change/nochange), and four other preferred land types
indxMap[pt] = (int(line[cNoChange]),olt2nlt[line[B_LU]] ,olt2nlt[line[C_LU]],olt2nlt[line[D_LU]],olt2nlt[line[E_LU]])
areaMap[pt] = ar
sumIndArea[temp_lt] += ar
copyLandType = landType
copySumIndArea = sumIndArea
area_sum=0.00
for i in range(43278):
if not i in areaMap:
areaMap[i]=0
for pt in areaMap.keys():
area_sum = area_sum + areaMap[pt]
# Loading area targets
gh = open("Tarets_File.csv","r")
datagh = gh.readlines()[1:]
for line in datagh:
line = line.split('\t')
#print line[0], line[1], line[2]
minA[int(line[0])] = float(line[1]) * area_sum
maxA[int(line[0])] = float(line[2]) * area_sum
#print line[0], minA[int(line[0])], maxA[int(line[0])], area_sum
#43151
self.dimensions = dim
self.objectives = 25
Benchmark.__init__(self, dimensions, 25) #numobj=3, dimensions=numcells=6
self.bounder = ec.DiscreteBounder([1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24])
self.maximize = True # Maximizing criteria: i.e. maximizing the objectives
self.adjList = adj_List
self.candidatesArea = area
self.landType = landType
self.copyLandType = copyLandType
self.areaMap = areaMap
self.indxMap = indxMap
self.sumIndArea = sumIndArea
self.copySumIndArea = copySumIndArea
self.total_area = area_sum
self.minA = minA
self.maxA = maxA
self.suit = suit
self.generator_count = generator_count
def __init__(self, dimensions=43278):
Raster = True
landtypes = {}
invertedlandtypes = {}
adj_List = {}
indxMap = {}
optimal = {}
typeArea = defaultdict(float)
roadDistance = {}
attractiveness = {}
devDistance = {}
b = {}
areaMap = {}
compatibility = defaultdict(float)
conversion = defaultdict(float)
landUseAllowed = 3
minA = defaultdict(float) # Minimum area for each land type (1-25)
maxA = defaultdict(float) # Max area for each land type
currentTypes = []
currentline = 0
f = open("mdata.txt", "r")
data = f.read().splitlines()
# First line species the number and land use types
l = data[currentline].split()
currentline = +1
n = int(l[0])
# Mapping for LandTypes
for i in range(1, n + 1):
landtypes[l[i]] = i - 1
invertedlandtypes[i - 1] = l[i]
# print landtypes
# number of parcels
parcels = int(data[currentline])
currentline = currentline + 1
# Adjacency List
for i in range(0, parcels):
line = data[currentline + i]
neighbours = line.split()
k = int(neighbours[0])
adj_List[k] = []
if (len(neighbours) > 1):
for neighbour in range(1, len(neighbours)):
j = int(neighbours[neighbour])
adj_List[k].append(j)
currentline = currentline + parcels
# print adj_List
parcels = int(data[currentline])
currentline = currentline + 1
# Land Use type Index
for i in range(0, parcels):
line = data[currentline + i]
allowedTypes = line.split()
k = int(allowedTypes[0])
indxMap[k] = []
# print i
currenttype = landtypes[allowedTypes[1]]
currentTypes.append(currenttype)
indxMap[k].append(currenttype)
isallowed = int(allowedTypes[2])
indxMap[k].append(isallowed)
if (len(allowedTypes) > 2):
for allowedType in range(landUseAllowed, len(allowedTypes)):
if allowedTypes[allowedType] in landtypes.keys():
j = landtypes[allowedTypes[allowedType]]
else:
j = landtypes["WF"]
indxMap[k].append(j)
currentline = currentline + parcels
# print currentTypes
n = int(data[currentline])
currentline = currentline + 1
# Land Use with min max areas and b
for i in range(0, n):
line = data[currentline + i]
allowedTypes = line.split()
currenttype = landtypes[allowedTypes[0]]
typeArea[currenttype] = 0
minA[currenttype] = float(allowedTypes[1])
maxA[currenttype] = float(allowedTypes[2])
b[currenttype] = allowedTypes[2]
currentline = currentline + n
# print minA
# print maxA
parcels = int(data[currentline])
currentline = currentline + 1
#Current Area
for i in range(0, parcels):
line = data[currentline + i]
allowedTypes = line.split()
k = int(allowedTypes[0])
currenttype = landtypes[allowedTypes[1]]
typeArea[currenttype] = typeArea[currenttype] + float(
allowedTypes[2])
areaMap[i] = float(allowedTypes[2])
currentline = currentline + parcels
# print 'Area*****'
# print typeArea
# parcels = int(data[currentline]);
# currentline = currentline + 1
parcels = int(data[currentline])
currentline = currentline + 1
#Road Distance and Attractiveness
for i in range(0, parcels):
line = data[currentline + i]
allowedTypes = line.split()
pt = int(allowedTypes[0])
attractiveness[pt] = float(allowedTypes[1])
#Road Distance and Attractiveness
# rasterFile = data[currentline].split()[2]
## print rasterFile
# rows=0
# columns=0
# rasterInputs = ''
# if data[currentline].split()[1] == 'Raster':
# Raster =True
# fr = open(rasterFile, "r")
# datar = fr.read().splitlines()
# rows = int(datar[0].split()[1])
# columns = int(datar[1].split()[1])
# for lines in range(0, 6):
# rasterInputs = rasterInputs + datar[lines] +"\n"
# print rasterInputs
Raster = False
# A_LU=3
# B_LU=4
# C_LU=5
# D_LU=6
# E_LU=7
# cSUIT=11
# cNoChange=9
# #id2idx = []
# #idx2id = []
# id2idx = {}
# idx2id = {}
# id2idx2 = {}
# idx2id2 ={}
# minA = {}
# maxA = {}
# suit=[]
# olt = {}
# for i in range(50000):
# suit.append(0)
#olt.append(0)
# olt2nlt = {}
# olt2nlt["AIR"] = 1
# olt2nlt["CIV"] = 2
# olt2nlt["HIC"] = 3
# olt2nlt["LIC"] = olt2nlt["HCC"] = 4
# olt2nlt["LLRN"] = olt2nlt["ER"] = 5
# olt2nlt["MFRN"] = 6
# olt2nlt["MHP"] = 7
# olt2nlt["MRN"] = 8
# olt2nlt["MUN"] = 9
# olt2nlt["NCC"] = 10
# olt2nlt["POS"]= 11
# olt2nlt["RCR"]= 12
# olt2nlt["RL"] = 13
# olt2nlt["SCC"]= 14
# olt2nlt["SLRN"] = 15
# olt2nlt["SOC"] = 16
# olt2nlt["TC"] = 17
# olt2nlt["UC"] = 18
# olt2nlt["UN"] = 19
# olt2nlt["VC"] = olt2nlt["HRR"] = 20
# olt2nlt["WF"] = 21
# olt2nlt["MUC"] = 22
# olt2nlt["MC"] = 23
# olt2nlt["TOD"] = 24
#Loading adj list for each cell
# f= open("Adj_List_Final.gal","r")
# data = f.read().splitlines()[1:]
# adj_List = {}
# for line in data:
# l = line.split(' ')
# if not l[0] is '':
# k = int(l[0])
# for i in range(len(l)):
# #print i, k
# j = int(l[i])
# if (i==0):
# if not k in adj_List:
# adj_List[k]=[]
# else:
# adj_List[k].append(j)
#
# if not j in adj_List:
# adj_List[j] =[]
# if not k in adj_List[j]:
# adj_List[j].append(k)
#print adj_List[20][1]
#Loading areas, landtype for each cell
# g=open("Refined_Swas_E_Updated2.csv","r")
# dim=0
# area = {}
# for i in range(0,25):
# sumIndArea.append(0)
# #print sumIndArea[21]
# datag = g.readlines()[1:]
# indxMap = {}
# areaMap = {}
# landType = []
# for line in datag:
# line = line.split('\t')
# pt = int(line[0])
# ar = float(line[2]) #area
# tee = line[1] # landtype
# temp_lt = olt2nlt[tee]
# landType.append(temp_lt)
# # indxMap : contains decision (change/nochange), and four preferred land types
# indxMap[pt] = (int(line[cNoChange]),olt2nlt[line[B_LU]] ,olt2nlt[line[C_LU]],olt2nlt[line[D_LU]],olt2nlt[line[E_LU]])
# areaMap[pt] = ar
# #print temp_lt
# sumIndArea[temp_lt] += ar
#print indxMap[20]
sumIndArea = defaultdict(float)
sumIndArea = typeArea
landType = currentTypes
copyLandType = copy.deepcopy(currentTypes)
copySumIndArea = copy.deepcopy(sumIndArea)
area_sum = 0.00
for i in range(43278):
if not i in areaMap:
areaMap[i] = 0
for pt in areaMap.keys():
area_sum = area_sum + areaMap[pt]
f = open("moptdata.txt", "r")
optData = f.read().splitlines()
currentline = 0
parcels = int(optData[currentline])
currentline = 1
# print optData[currentline];
# Land Use type Index
for i in range(0, parcels):
line = optData[currentline + i]
# print line
allowedTypes = line.split()
k = int(allowedTypes[0])
optimal[k] = []
# print allowedTypes
isallowed = int(allowedTypes[1])
optimal[k].append(isallowed)
optimal[k].append(landtypes[allowedTypes[2]])
# print optimal[k]
# currentline = currentline + parcels
# Loading area targets
# gh = open("SWAS_E_LU_PERCENT.csv","r")
# datagh = gh.readlines()
# for line in datagh:
# line = line.split('\t')
# #print line[0], line[1], line[2]
# minA[int(line[0])] = float(line[1]) * area_sum
# maxA[int(line[0])] = float(line[2]) * area_sum
#print line[0], minA[int(line[0])], maxA[int(line[0])], area_sum
rasterInputs = 1
rows = 1
columns = 1
#43151
self.dimensions = parcels
self.objectives = 25
Benchmark.__init__(self, parcels, 25) #numobj=3, dimensions=numcells=6
#self.bounder = ec.Bounder([0] * self.dimensions, [3.0] * self.dimensions)
self.bounder = ec.DiscreteBounder([
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
20, 21, 22, 23, 24
])
self.maximize = True
#self.candidatesArea = {0:10,1:10,2:10,3:10,4:10,5:10}
#self.total_area = 60
#self.adjList = {0:[1,2],1:[0,2],2:[0,1,3],3:[2,4,5],4:[3,5],5:[3,4]}
self.adjList = adj_List
# self.candidatesArea = area
self.landType = landType
self.copyLandType = copyLandType
self.areaMap = areaMap
self.indxMap = indxMap
self.optimal = optimal
self.sumIndArea = sumIndArea
self.copySumIndArea = copySumIndArea
self.total_area = area_sum
self.minA = minA
self.maxA = maxA
self.attractiveness = attractiveness
self.devDistance = devDistance
self.conversion = conversion
# self.suit = suit
self.Raster = Raster
self.rasterVal = rasterInputs
self.rows = rows
self.columns = columns
# http://people.hofstra.edu/geotrans/eng/ch6en/conc6en/ch6c2en.html
for i in range(24):
print 'Type********', i
print self.minA[i]
print self.maxA[i]
print self.sumIndArea[i]
def __init__(self, dimensions=43278):
A_LU = 3
B_LU = 4
C_LU = 5
D_LU = 6
E_LU = 7
cSUIT = 11
cNoChange = 9
#id2idx = []
#idx2id = []
id2idx = {}
idx2id = {}
id2idx2 = {}
idx2id2 = {}
minA = {}
maxA = {}
suit = []
olt = {}
for i in range(50000):
suit.append(0)
#olt.append(0)
'''
0: AIR: Airport, 1: CIV: Civic, 2:POS: Preserved Open Space
3:WF, Working Farm
3:RL, Rural Living
3:RCR, Rural Crossroads
4:ER, Estate Residential
4:LLRN, Large Lot Residential Neighborhood
5:SLRN, Small Lot Residential Neighborhood
5:MFRN, Multifamily Residential Neighborhood
5:MRN, Mixed Residential Neighborhood
5:MHP
6:HRR, Healthcare
7:NCC, Neighborhood Commercial Center
7:SCC, Suburban Commercial Corridor
7:SOC, Suburban Office Center
8:LIC, Light Industrial
8:HIC, Heavy Industrial
8:HCC, Highway Commercial Corridor
9:MUN, Mixed-Use Neighborhood
9:TC, Town Center
9:UN, Urban Neighborhood
9:TOD, Transit Oriented Development
9:MUC, Mixed Use Center
9:MC, Metropolitan Center
9:UC, Urban Center
'''
olt2nlt = {}
olt2nlt["AIR"] = 1
olt2nlt["CIV"] = 2
olt2nlt["HIC"] = 3
olt2nlt["LIC"] = olt2nlt["HCC"] = 4
olt2nlt["LLRN"] = olt2nlt["ER"] = 5
olt2nlt["MFRN"] = 6
olt2nlt["MHP"] = 7
olt2nlt["MRN"] = 8
olt2nlt["MUN"] = 9
olt2nlt["NCC"] = 10
olt2nlt["POS"] = 11
olt2nlt["RCR"] = 12
olt2nlt["RL"] = 13
olt2nlt["SCC"] = 14
olt2nlt["SLRN"] = 15
olt2nlt["SOC"] = 16
olt2nlt["TC"] = 17
olt2nlt["UC"] = 18
olt2nlt["UN"] = 19
olt2nlt["VC"] = olt2nlt["HRR"] = 20
olt2nlt["WF"] = 21
olt2nlt["MUC"] = 22
olt2nlt["MC"] = 23
olt2nlt["TOD"] = 24
#Loading adj list for each cell
f = open("Adj_List_Final.gal", "r")
data = f.read().splitlines()[1:]
adj_List = {}
for line in data:
l = line.split(' ')
if not l[0] is '':
k = int(l[0])
for i in range(len(l)):
#print i, k
j = int(l[i])
if (i == 0):
if not k in adj_List:
adj_List[k] = []
else:
adj_List[k].append(j)
if not j in adj_List:
adj_List[j] = []
if not k in adj_List[j]:
adj_List[j].append(k)
#print adj_List[20][1]
#Loading areas, landtype for each cell
g = open("Refined_Swas_E_Updated2.csv", "r")
dim = 0
area = {}
sumIndArea = []
for i in range(0, 25):
sumIndArea.append(0)
#print sumIndArea[21]
datag = g.readlines()[1:]
indxMap = {}
areaMap = {}
landType = []
for line in datag:
line = line.split('\t')
pt = int(line[0])
ar = float(line[2]) #area
tee = line[1] # landtype
temp_lt = olt2nlt[tee]
landType.append(temp_lt)
# indxMap : contains decision (change/nochange), and four preferred land types
indxMap[pt] = (int(line[cNoChange]), olt2nlt[line[B_LU]],
olt2nlt[line[C_LU]], olt2nlt[line[D_LU]],
olt2nlt[line[E_LU]])
areaMap[pt] = ar
#print temp_lt
sumIndArea[temp_lt] += ar
#print indxMap[20]
copyLandType = landType
copySumIndArea = sumIndArea
area_sum = 0.00
for i in range(43278):
if not i in areaMap:
areaMap[i] = 0
for pt in areaMap.keys():
area_sum = area_sum + areaMap[pt]
# Loading area targets
gh = open("SWAS_E_LU_PERCENT.csv", "r")
datagh = gh.readlines()
for line in datagh:
line = line.split('\t')
#print line[0], line[1], line[2]
minA[int(line[0])] = float(line[1]) * area_sum
maxA[int(line[0])] = float(line[2]) * area_sum
#print line[0], minA[int(line[0])], maxA[int(line[0])], area_sum
#43151
self.dimensions = dim
self.objectives = 25
Benchmark.__init__(self, dimensions,
25) #numobj=3, dimensions=numcells=6
#self.bounder = ec.Bounder([0] * self.dimensions, [3.0] * self.dimensions)
self.bounder = ec.DiscreteBounder([
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
20, 21, 22, 23, 24
])
self.maximize = True
#self.candidatesArea = {0:10,1:10,2:10,3:10,4:10,5:10}
#self.total_area = 60
#self.adjList = {0:[1,2],1:[0,2],2:[0,1,3],3:[2,4,5],4:[3,5],5:[3,4]}
self.adjList = adj_List
self.candidatesArea = area
self.landType = landType
self.copyLandType = copyLandType
self.areaMap = areaMap
self.indxMap = indxMap
self.sumIndArea = sumIndArea
self.copySumIndArea = copySumIndArea
self.total_area = area_sum
self.minA = minA
self.maxA = maxA
self.suit = suit
def NSGA2():
"""Starts the optimization with the NSGA-II algorithm."""
begin = time.time()
# initialize random generator with system time
rand = random.Random()
rand.seed()
# Generate the original start individual from input data
# return it including the non static land use indices
start_individual, nonstatic_elements = generate_genom(max_range, file_HRU,cfg.mapConfig.file_ASCII_map,
cfg.mapConfig.file_transformation, cfg.mapConfig.file_ID_map,
cfg.mapConfig.four_neighbours)
if len(start_individual) == 0:
msg = "Error: The generated start individual has no elements."
WriteLogMsg(msg)
raise SystemError("Error: The generated start individual has no elements.")
close_window
# determine that 'Bounder' conditions of candidates are equal to
# the integer values of the non static land use indices
bounder_discrete = nonstatic_elements
# initialize inspyred log files
stats_file,individ_file = fh.init_inspyred_logfiles()
# initialize and run NSGA2
ea = ec.emo.NSGA2(rand)
# public attributes
# NSGA2 is predefined with tournament_selection
if cfg.ea.selector != 'tournament_selection':
exec ("%s%s" % ('ea.selector = ', fh.preparing_attribute('selector',cfg.ea.selector)))
msg = 'Selector of the optimization algorithm changed to: %s' % cfg.ea.selector
WriteLogMsg(msg)
# NSGA2 is predefined with nsga_replacement
if cfg.ea.replacer != 'nsga_replacement':
exec ("%s%s" % ('ea.replacer = ', fh.preparing_attribute('replacer',cfg.ea.replacer)))
msg = 'Replacer of the optimization algorithm changed to: %s' % cfg.ea.replacer
WriteLogMsg(msg)
# NSGA2 is predefined with best_archiver
if cfg.ea.archiver != 'best_archiver':
exec ("%s%s" % ('ea.archiver = ', fh.preparing_attribute('archiver',cfg.ea.archiver)))
msg = 'Archiver of the optimization algorithm changed to: %s' % cfg.ea.archiver
WriteLogMsg(msg)
exec ("%s%s" % ('ea.migrator = ', fh.preparing_attribute('migrator',cfg.ea.migrator)))
# file observer prints after each generation the best, worst, mean etc. values into the statistic and individual file
exec ("%s%s" % ('ea.observer = ', fh.preparing_attribute('observer',cfg.ea.observer)))
# specify how the new candidates should be varied
exec ("%s%s" % ('ea.variator = ', fh.preparing_attribute('variator',cfg.ea.variator)))
# specify when the optimization should terminate
exec ("%s%s" % ('ea.terminator = ', fh.preparing_attribute('terminator',cfg.ea.terminator)))
# NSGA2 is predefined with num_selected = pop_size
if cfg.ea.num_selected != cfg.ea.pop_size:
msg = 'Num_selected of the optimization algorithm changed to: %s' % cfg.ea.num_selected
WriteLogMsg(msg)
# NSGA2 is predefined with tournament_size = 2
if cfg.ea.tournament_size != 2:
msg = 'Tournament_size of the optimization algorithm changed to: %s' % cfg.ea.tournament_size
WriteLogMsg(msg)
# run optimization, when finished final_pop holds the results
final_pop = ea.evolve(generator = generate_parameter,
# evaluate is the function to start external models
# return results for the optimization algorithm
evaluator = evaluate,
# define population size
pop_size = cfg.ea.pop_size,
# maximize or Minimize the problem (default True)
maximize = cfg.ea.maximize,
# bound the parameters to an interval
# DiscreteBounder: choose integer values between 1 and max_range
bounder = ec.DiscreteBounder(bounder_discrete),
# minimum population diversity allowed (when using diversity_termination default 0.001)
min_diversity = cfg.ea.min_diversity,
# maximum number of generations
max_generations = cfg.ea.max_generations,
# maximum number of evaluations (default pop_size)
max_evaluations = cfg.ea.max_evaluations,
# number of elites to consider (default 0)
num_elites = cfg.ea.num_elites,
# number of individuals to be selected (default NSGA2 pop_size)
num_selected = cfg.ea.num_selected,
# tournament size (default NSGA2 2)
tournament_size = cfg.ea.tournament_size,
# rate at which crossover is performed (default 1.0)
crossover_rate = cfg.ea.crossover_rate,
# rate at which mutation is performed (default 0.1)
mutation_rate = cfg.ea.mutation_rate,
# number of crossover points used (default 1)
num_crossover_points = cfg.ea.num_crossover_points,
# a positive integer representing the number of
# closest solutions to consider as a “crowd” (default 2)
crowding_distance = cfg.ea.crowding_distance,
# statistic file
statistics_file = stats_file,
# individuals file
individuals_file = individ_file)
final_arc = ea.archive
# for constrained_tournament_selection:
# create a copy of final_arc only with feasible individuals (for csv file with best feasible solutions)
if 'constrained_tournament_selection' in cfg.ea.selector:
final_arc_feasible = []
end = time.time()
msg = "The optimization process needed %d seconds." %(end-begin)
fh.WriteLogMsg(msg)
msg = 'Best Solutions: \n'
WriteLogMsg(msg)
f_count=1
for f in final_arc:
# for constrained_tournament_selection: with information if individual is infeasible
# and copy feasible solutions in final_arc_feasible
if 'constrained_tournament_selection' in cfg.ea.selector:
if individual_filter(f.candidate) == False:
WriteLogMsg("(infeasible) %s" % f)
# save the map as ascii file in output folder
if file_HRU == 'None' or (file_HRU != 'None' and cfg.mapConfig.file_ID_map != 'None'):
transform_individual_ascii_map(f.candidate,False,f_count,None,None,None,False)
else:
WriteLogMsg("%s" % f)
# save the map as ascii file in output folder
if file_HRU == 'None' or (file_HRU != 'None' and cfg.mapConfig.file_ID_map != 'None'):
transform_individual_ascii_map(f.candidate,False,f_count)
final_arc_feasible.append(f)
else:
msg = "%s" % f
#msg = "%f" % f
WriteLogMsg(msg)
# save the map as ascii file in output folder
if file_HRU == 'None' or (file_HRU != 'None' and cfg.mapConfig.file_ID_map != 'None'):
transform_individual_ascii_map(f.candidate,False,f_count)
if f_count == 1:
len_fitness = len(f.fitness)
f_count += 1
# plot the best solution in a 2, 3 or 4 dimensional plot
# 2 dimensional plot
if (cfg.ea.plot_results == True and len_fitness == 2):
# log the best solutions in a csv file
fh.save_best_solutions(final_arc,2)
import pylab
x = []
y = []
for f in final_arc:
x.append(f.fitness[0])
y.append(f.fitness[1])
pylab.scatter(x, y, color='r')
fh.savePlot_png(opt_algorithm)
pylab.show()
# for constrained_tournament_selection: create a second plot with feasible solutions
if 'constrained_tournament_selection' in cfg.ea.selector:
# log the best feasible solutions in a csv file
fh.save_best_solutions(final_arc_feasible,2)
x = []
y = []
for f in final_arc_feasible:
x.append(f.fitness[0])
y.append(f.fitness[1])
pylab.scatter(x, y, color='r')
fh.savePlot_png(opt_algorithm)
pylab.show()
# 3 and 4 dimensional plots
if (cfg.ea.plot_results == True and (len_fitness == 3 or len_fitness == 4)):
import warnings
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=FutureWarning)
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
x = []
y = []
if len_fitness == 3 or len_fitness == 4:
z = []
if len_fitness == 4:
c = []
for f in final_arc:
x.append(f.fitness[0])
y.append(f.fitness[1])
if len(f.fitness) == 3 or len(f.fitness) == 4:
z.append(f.fitness[2])
if len(f.fitness) == 4:
c.append(f.fitness[3])
if len_fitness == 3:
# log the best solutions in a csv file
fh.save_best_solutions(final_arc,3)
ax.scatter(x, y, z, c='r')
if len_fitness == 4:
# log the best solutions in a csv file
fh.save_best_solutions(final_arc,4)
ax.scatter(x, y, z, c=c, cmap=plt.hot())
fh.savePlot_png(opt_algorithm)
plt.show()
# for constrained_tournament_selection: create a second plot with feasible solutions
if 'constrained_tournament_selection' in cfg.ea.selector:
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
x = []
y = []
if len_fitness == 3 or len_fitness == 4:
z = []
if len_fitness == 4:
c = []
for f in final_arc_feasible:
x.append(f.fitness[0])
y.append(f.fitness[1])
if len(f.fitness) == 3 or len(f.fitness) == 4:
z.append(f.fitness[2])
if len(f.fitness) == 4:
c.append(f.fitness[3])
if len_fitness == 3:
# log the best solutions in a csv file
fh.save_best_solutions(final_arc_feasible,3)
ax.scatter(x, y, z, c='r')
if len_fitness == 4:
# log the best solutions in a csv file
fh.save_best_solutions(final_arc_feasible,4)
ax.scatter(x, y, z, c=c, cmap=plt.hot())
fh.savePlot_png(opt_algorithm)
plt.show()
# print results without plotting (if plot_results was set to false)
if cfg.ea.plot_results == False:
if len_fitness == 2:
# log the best solutions in a csv file
fh.save_best_solutions(final_arc,2)
if 'constrained_tournament_selection' in cfg.ea.selector:
# log the best feasible solutions in a csv file
fh.save_best_solutions(final_arc_feasible,2)
if len_fitness == 3:
# log the best solutions in a csv file
fh.save_best_solutions(final_arc,3)
if 'constrained_tournament_selection' in cfg.ea.selector:
# log the best feasible solutions in a csv file
fh.save_best_solutions(final_arc_feasible,3)
if len_fitness == 4:
# log the best solutions in a csv file
fh.save_best_solutions(final_arc,4)
if 'constrained_tournament_selection' in cfg.ea.selector:
# log the best feasible solutions in a csv file
fh.save_best_solutions(final_arc_feasible,4)
def GA():
"""Starts the optimization with the GA algorithm."""
begin = time.time()
# initialize random generator with system time
rand = random.Random()
rand.seed()
# original start individual of the input data
global start_individual
# generate the original start individual from the input data
# return it including the non static land use indices
start_individual, nonstatic_elements = generate_genom(max_range, file_HRU,cfg.mapConfig.file_ASCII_map,
cfg.mapConfig.file_transformation, cfg.mapConfig.file_ID_map,
cfg.mapConfig.four_neighbours)
if len(start_individual) == 0:
msg = "Error: The generated start individual has no elements."
WriteLogMsg(msg)
raise SystemError("Error: The generated start individual has no elements.")
close_window
# determine that 'Bounder' conditions of candidates are equal to
# the integer values of the non static land use indices
bounder_discrete = nonstatic_elements
# initialize inspyred log files
stats_file,individ_file = fh.init_inspyred_logfiles()
# initialize and run GA
ea = ec.GA(rand)
# public attributes
# GA is predefined with rank_selection
if cfg.ea.selector != 'rank_selection':
exec ("%s%s" % ('ea.selector = ', fh.preparing_attribute('selector',cfg.ea.selector)))
msg = 'Selector of the optimization algorithm changed to: %s' % cfg.ea.selector
WriteLogMsg(msg)
# GA is predefined with generational_replacement
if cfg.ea.replacer != 'generational_replacement':
exec ("%s%s" % ('ea.replacer = ', fh.preparing_attribute('replacer',cfg.ea.replacer)))
msg = 'Replacer of the optimization algorithm changed to: %s' % cfg.ea.replacer
WriteLogMsg(msg)
# specify how the new candidates should be varied
# GA is predefined with n_point_crossover,bit_flip_mutation as variators
if cfg.ea.variator != 'n_point_crossover,bit_flip_mutation' and cfg.ea.variator != 'bit_flip_mutation,n_point_crossover':
exec ("%s%s" % ('ea.variator = ', fh.preparing_attribute('variator',cfg.ea.variator)))
msg = 'Variator of the optimization algorithm changed to: %s' % cfg.ea.variator
WriteLogMsg(msg)
# GA is predefined with num_selected = pop_size
if cfg.ea.num_selected != cfg.ea.pop_size:
msg = 'Num_selected of the optimization algorithm changed to: %s' % cfg.ea.num_selected
WriteLogMsg(msg)
exec ("%s%s" % ('ea.migrator = ', fh.preparing_attribute('migrator',cfg.ea.migrator)))
exec ("%s%s" % ('ea.archiver = ', fh.preparing_attribute('archiver',cfg.ea.archiver)))
if cfg.ea.archiver != 'best_archiver':
msg = 'Archiver of the optimization algorithm changed to: %s' % cfg.ea.archiver
WriteLogMsg(msg)
exec ("%s%s" % ('ea.observer = ', fh.preparing_attribute('observer',cfg.ea.observer)))
# specify when the optimization should terminate
exec ("%s%s" % ('ea.terminator = ', fh.preparing_attribute('terminator',cfg.ea.terminator)))
# run optimization, when finished final_pop holds the results
final_pop = ea.evolve(generator = generate_parameter,
# evaluate is the function to start external models
# return results for the optimization algorithm
evaluator = evaluate,
# define population size
pop_size = cfg.ea.pop_size,
# maximize or minimize the problem
maximize = cfg.ea.maximize,
# bound the parameters to an interval
# choose integer values between 1 and max_range in this case
bounder = ec.DiscreteBounder(bounder_discrete),
# minimum population diversity allowed (when using diversity_termination default 0.001)
min_diversity = cfg.ea.min_diversity,
# maximum number of evaluations (default pop_size)
max_evaluations = cfg.ea.max_evaluations,
# maximum number of generations
max_generations = cfg.ea.max_generations,
# number of elites to consider (default 0)
num_elites = cfg.ea.num_elites,
# number of individuals to be selected (default NSGA2 pop_size)
num_selected = cfg.ea.num_selected,
# tournament size (default NSGA2 2)
tournament_size = cfg.ea.tournament_size,
# the rate at which crossover is performed (default 1.0)
crossover_rate = cfg.ea.crossover_rate,
# mutation rate
mutation_rate = cfg.ea.mutation_rate,
# number of crossover points used (default 1)
num_crossover_points = cfg.ea.num_crossover_points,
# a positive integer representing the number of
# closest solutions to consider as a “crowd” (default 2)
crowding_distance = cfg.ea.crowding_distance,
# statistic file
statistics_file = stats_file,
# individuals file
individuals_file = individ_file)
# read out the best individuals
final_arc = ea.archive
# for constrained_tournament_selection:
# create a copy of final_arc only with feasible individuals (for csv file with best feasible solutions)
if 'constrained_tournament_selection' in cfg.ea.selector:
final_arc_feasible = []
end = time.time()
WriteLogMsg("The optimization process needed %d seconds." %(end-begin))
msg = 'Best Solutions: \n'
WriteLogMsg(msg)
# save the map as ascii file in output folder
f_count=1
for f in final_arc:
# for constrained_tournament_selection: with information if individual is infeasible
# and copy feasible solutions in final_arc_feasible
if 'constrained_tournament_selection' in cfg.ea.selector:
if individual_filter(f.candidate) == False:
WriteLogMsg("(infeasible) %s" % f)
# save the map as ascii file in output folder
if file_HRU == 'None' or (file_HRU != 'None' and cfg.mapConfig.file_ID_map != 'None'):
transform_individual_ascii_map(f.candidate,False,f_count,None,None,None,False)
else:
WriteLogMsg("%s" % f)
# save the map as ascii file in output folder
if file_HRU == 'None' or (file_HRU != 'None' and cfg.mapConfig.file_ID_map != 'None'):
transform_individual_ascii_map(f.candidate,False,f_count)
final_arc_feasible.append(f)
else:
WriteLogMsg("%s" % f)
# save the map as ascii file in output folder
if file_HRU == 'None' or (file_HRU != 'None' and cfg.mapConfig.file_ID_map != 'None'):
transform_individual_ascii_map(f.candidate,False,f_count)
f_count += 1
if cfg.ea.maximize == 'True':
if 'constrained_tournament_selection' in cfg.ea.selector and individual_filter(f.candidate) == False:
WriteLogMsg("\nFinal infeasible individual: %s, [%f]" % (max(final_pop).candidate,max(final_pop).fitness))
else:
WriteLogMsg("\nFinal individual: %s, [%f]" % (max(final_pop).candidate,max(final_pop).fitness))
else:
if 'constrained_tournament_selection' in cfg.ea.selector and individual_filter(f.candidate) == False:
WriteLogMsg("\nFinal infeasible individual: %s, [%f]" % (min(final_pop).candidate,min(final_pop).fitness))
else:
WriteLogMsg("\nFinal individual: %s, [%f]" % (min(final_pop).candidate,min(final_pop).fitness))
# save the map as ascii file in output folder
# log the best solutions in a csv file
fh.save_best_solutions(final_arc,1)
# for constrained_tournament_selection: log the best feasible solutions in a csv file
if 'constrained_tournament_selection' in cfg.ea.selector:
fh.save_best_solutions(final_arc_feasible,1)
def __init___(self, dimensions=4):
problem.__init__(self, dimensions, 2)
self.bounder = ec.DiscreteBounder([0, 1])
self.maximze = True
def __init__(self, dimensions=43278):
landtypes = {}
invertedlandtypes = {}
adj_List = {}
indxMap = {}
typeArea = defaultdict(float)
roadDistance = {}
attractiveness = {}
b = {}
areaMap = {}
compatibility = defaultdict(float)
conversion = defaultdict(float)
landUseAllowed = 3
minA = defaultdict(float) # Minimum area for each land type (1-25)
maxA = defaultdict(float) # Max area for each land type
currentTypes = []
currentline = 0
f = open("sample", "r")
data = f.read().splitlines()
# First line species the number and land use types
l = data[currentline].split()
currentline = +1
n = int(l[0])
# Mapping for LandTypes
for i in range(1, n + 1):
landtypes[l[i]] = i - 1
invertedlandtypes[i - 1] = l[i]
print landtypes
# number of parcels
parcels = int(data[currentline])
currentline = currentline + 1
# Adjacency List
for i in range(0, parcels):
line = data[currentline + i]
neighbours = line.split()
k = int(neighbours[0])
adj_List[k] = []
if (len(neighbours) > 1):
for neighbour in range(1, len(neighbours)):
j = int(neighbours[neighbour])
adj_List[k].append(j)
currentline = currentline + parcels
print adj_List
parcels = int(data[currentline])
currentline = currentline + 1
# Land Use type Index
for i in range(0, parcels):
line = data[currentline + i]
allowedTypes = line.split()
k = int(allowedTypes[0])
indxMap[k] = []
currenttype = landtypes[allowedTypes[1]]
currentTypes.append(currenttype)
indxMap[k].append(currenttype)
isallowed = int(allowedTypes[2])
indxMap[k].append(isallowed)
if (len(allowedTypes) > 2):
for allowedType in range(landUseAllowed, len(allowedTypes)):
j = landtypes[allowedTypes[allowedType]]
indxMap[k].append(j)
currentline = currentline + parcels
print indxMap
n = int(data[currentline])
currentline = currentline + 1
# Land Use with min max areas and b
for i in range(0, n):
line = data[currentline + i]
allowedTypes = line.split()
currenttype = landtypes[allowedTypes[0]]
typeArea[currenttype] = 0
minA[currenttype] = float(allowedTypes[1])
maxA[currenttype] = float(allowedTypes[2])
b[currenttype] = allowedTypes[2]
currentline = currentline + n
print minA
print maxA
parcels = int(data[currentline])
currentline = currentline + 1
#Current Area
for i in range(0, parcels):
line = data[currentline + i]
allowedTypes = line.split()
k = int(allowedTypes[0])
currenttype = landtypes[allowedTypes[1]]
typeArea[currenttype] = typeArea[currenttype] + int(
allowedTypes[2])
areaMap[i] = int(allowedTypes[2])
currentline = currentline + parcels
print typeArea
parcels = int(data[currentline])
currentline = currentline + 1
#Road Distance and Attractiveness
for i in range(0, parcels):
line = data[currentline + i]
allowedTypes = line.split()
pt = int(allowedTypes[0])
roadDistance[pt] = int(allowedTypes[1])
attractiveness[pt] = float(allowedTypes[2])
currentline = currentline + parcels
#Compatibility Matrix
for type1 in range(0, n):
line = data[currentline + type1]
compats = line.split()
for type2 in range(0, n):
compatibility[type1, type2] = compats[type2]
currentline = currentline + n
print compatibility
#Conversion Matrix
for type1 in range(0, n):
line = data[currentline + type1]
converts = line.split()
for type2 in range(0, n):
conversion[type1, type2] = converts[type2]
currentline = currentline + n
print conversion
# A_LU=3
# B_LU=4
# C_LU=5
# D_LU=6
# E_LU=7
# cSUIT=11
# cNoChange=9
# #id2idx = []
# #idx2id = []
# id2idx = {}
# idx2id = {}
# id2idx2 = {}
# idx2id2 ={}
# minA = {}
# maxA = {}
# suit=[]
# olt = {}
# for i in range(50000):
# suit.append(0)
#olt.append(0)
# olt2nlt = {}
# olt2nlt["AIR"] = 1
# olt2nlt["CIV"] = 2
# olt2nlt["HIC"] = 3
# olt2nlt["LIC"] = olt2nlt["HCC"] = 4
# olt2nlt["LLRN"] = olt2nlt["ER"] = 5
# olt2nlt["MFRN"] = 6
# olt2nlt["MHP"] = 7
# olt2nlt["MRN"] = 8
# olt2nlt["MUN"] = 9
# olt2nlt["NCC"] = 10
# olt2nlt["POS"]= 11
# olt2nlt["RCR"]= 12
# olt2nlt["RL"] = 13
# olt2nlt["SCC"]= 14
# olt2nlt["SLRN"] = 15
# olt2nlt["SOC"] = 16
# olt2nlt["TC"] = 17
# olt2nlt["UC"] = 18
# olt2nlt["UN"] = 19
# olt2nlt["VC"] = olt2nlt["HRR"] = 20
# olt2nlt["WF"] = 21
# olt2nlt["MUC"] = 22
# olt2nlt["MC"] = 23
# olt2nlt["TOD"] = 24
#Loading adj list for each cell
# f= open("Adj_List_Final.gal","r")
# data = f.read().splitlines()[1:]
# adj_List = {}
# for line in data:
# l = line.split(' ')
# if not l[0] is '':
# k = int(l[0])
# for i in range(len(l)):
# #print i, k
# j = int(l[i])
# if (i==0):
# if not k in adj_List:
# adj_List[k]=[]
# else:
# adj_List[k].append(j)
#
# if not j in adj_List:
# adj_List[j] =[]
# if not k in adj_List[j]:
# adj_List[j].append(k)
#print adj_List[20][1]
#Loading areas, landtype for each cell
# g=open("Refined_Swas_E_Updated2.csv","r")
# dim=0
# area = {}
# for i in range(0,25):
# sumIndArea.append(0)
# #print sumIndArea[21]
# datag = g.readlines()[1:]
# indxMap = {}
# areaMap = {}
# landType = []
# for line in datag:
# line = line.split('\t')
# pt = int(line[0])
# ar = float(line[2]) #area
# tee = line[1] # landtype
# temp_lt = olt2nlt[tee]
# landType.append(temp_lt)
# # indxMap : contains decision (change/nochange), and four preferred land types
# indxMap[pt] = (int(line[cNoChange]),olt2nlt[line[B_LU]] ,olt2nlt[line[C_LU]],olt2nlt[line[D_LU]],olt2nlt[line[E_LU]])
# areaMap[pt] = ar
# #print temp_lt
# sumIndArea[temp_lt] += ar
#print indxMap[20]
sumIndArea = defaultdict(float)
sumIndArea = typeArea
landType = currentTypes
copyLandType = landType
copySumIndArea = sumIndArea
area_sum = 0.00
for i in range(43278):
if not i in areaMap:
areaMap[i] = 0
for pt in areaMap.keys():
area_sum = area_sum + areaMap[pt]
# Loading area targets
# gh = open("SWAS_E_LU_PERCENT.csv","r")
# datagh = gh.readlines()
# for line in datagh:
# line = line.split('\t')
# #print line[0], line[1], line[2]
# minA[int(line[0])] = float(line[1]) * area_sum
# maxA[int(line[0])] = float(line[2]) * area_sum
#print line[0], minA[int(line[0])], maxA[int(line[0])], area_sum
#43151
self.dimensions = parcels
self.objectives = 25
Benchmark.__init__(self, parcels, 25) #numobj=3, dimensions=numcells=6
#self.bounder = ec.Bounder([0] * self.dimensions, [3.0] * self.dimensions)
self.bounder = ec.DiscreteBounder([
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
20, 21, 22, 23, 24
])
self.maximize = True
#self.candidatesArea = {0:10,1:10,2:10,3:10,4:10,5:10}
#self.total_area = 60
#self.adjList = {0:[1,2],1:[0,2],2:[0,1,3],3:[2,4,5],4:[3,5],5:[3,4]}
self.adjList = adj_List
# self.candidatesArea = area
self.landType = landType
self.copyLandType = copyLandType
self.areaMap = areaMap
self.indxMap = indxMap
self.sumIndArea = sumIndArea
self.copySumIndArea = copySumIndArea
self.total_area = area_sum
self.minA = minA
self.maxA = maxA
def __init___(self):
problem.__init__(self, 2)
self.bounder = ec.DiscreteBounder([0, 1])
self.maximze = True
