def MOOSetUp (allm_var):
import pandas as pd
from nsga_2 import nsga_2
input_v = {}
##These are parameters for the genetic algorithm to work
dim_allm_var = allm_var.shape
input_v['population'] = 50
input_v['generations'] = 500
input_v['number_of_objectives'] = 2
input_v['number_of_decision_variables'] = dim_allm_var[0]
##List of objective functions values
obj_v = pd.DataFrame(columns = ['Value'])
##1
add_obj_v = {}
add_obj_v['Value'] = 0
input_add_obj_v = [add_obj_v['Value']]
input_add_obj_vdf = pd.DataFrame(data = [input_add_obj_v], columns = ['Value'])
obj_v = obj_v.append(input_add_obj_vdf, ignore_index=True)
##2
add_obj_v = {}
add_obj_v['Value'] = 0
input_add_obj_v = [add_obj_v['Value']]
input_add_obj_vdf = pd.DataFrame(data = [input_add_obj_v], columns = ['Value'])
obj_v = obj_v.append(input_add_obj_vdf, ignore_index=True)
input_v['obj_func_values'] = obj_v
##List of decision variables and their respective ranges
dv = pd.DataFrame(columns = ['Name', 'Initial_value', 'Unit', 'Min', 'Max', 'Type'])
for i in range (0,dim_allm_var[0]):
add_dv = {}
add_dv['Name'] = allm_var['Name'][i]
add_dv['Initial_value'] = allm_var['Value'][i]
add_dv['Unit'] = allm_var['Unit'][i]
add_dv['Min'] = allm_var['LB'][i]
add_dv['Max'] = allm_var['UB'][i]
add_dv['Type'] = allm_var['Type'][i]
input_add_dv = [add_dv['Name'], add_dv['Initial_value'], add_dv['Unit'], add_dv['Min'], add_dv['Max'], add_dv['Type']]
input_add_dvdf = pd.DataFrame(data = [input_add_dv], columns = ['Name', 'Initial_value', 'Unit', 'Min', 'Max', 'Type'])
dv = dv.append(input_add_dvdf, ignore_index=True)
input_v['range_of_decision_variables'] = dv
##Input the data into NSGA=II
nsga_2(input_v)
return
def MOOSetUp():
import pandas as pd
from nsga_2 import nsga_2
allm_var = pd.DataFrame(
columns=['Name', 'Value', 'Unit', 'LB', 'UB', 'Type'])
activate_parallel = 'no'
num_cores = 4
##Crossover pool percentage (size of the parent_pool) (best to keep above 0.2 and less than 0.6)
crossover_perc = 0.3
##Mutation percentage (variation, not corrpution, best to keep between 0.05 and 0.1)
mutation_perc = 0.1
v1 = ['x1', 'continuous', -100, 100, 3, '-']
v2 = ['x2', 'continuous', -100, 100, 3, '-']
t11 = pd.DataFrame(data=[v1],
columns=[
'Name', 'Type', 'Lower_bound', 'Upper_bound',
'Bin_dec_prec', 'Steps'
])
t12 = pd.DataFrame(data=[v2],
columns=[
'Name', 'Type', 'Lower_bound', 'Upper_bound',
'Bin_dec_prec', 'Steps'
])
allm_var = allm_var.append(t11, ignore_index=True)
allm_var = allm_var.append(t12, ignore_index=True)
input_v = {}
##These are parameters for the genetic algorithm to work
dim_allm_var = [2]
input_v['population'] = 100
input_v['generations'] = 50
input_v['number_of_objectives'] = 2
input_v['number_of_decision_variables'] = dim_allm_var[0]
##List of objective functions values
obj_v = pd.DataFrame(columns=['Value'])
##1
add_obj_v = {}
add_obj_v['Value'] = 0
input_add_obj_v = [add_obj_v['Value']]
input_add_obj_vdf = pd.DataFrame(data=[input_add_obj_v], columns=['Value'])
obj_v = obj_v.append(input_add_obj_vdf, ignore_index=True)
##2
add_obj_v = {}
add_obj_v['Value'] = 0
input_add_obj_v = [add_obj_v['Value']]
input_add_obj_vdf = pd.DataFrame(data=[input_add_obj_v], columns=['Value'])
obj_v = obj_v.append(input_add_obj_vdf, ignore_index=True)
input_v['obj_func_values'] = obj_v
##List of decision variables and their respective ranges
dv = pd.DataFrame(columns=[
'Name', 'Type', 'Lower_bound', 'Upper_bound', 'Bin_dec_prec', 'Steps'
])
for i in range(0, dim_allm_var[0]):
add_dv = {}
add_dv['Name'] = allm_var['Name'][i]
add_dv['Type'] = allm_var['Type'][i]
add_dv['Lower_bound'] = allm_var['Lower_bound'][i]
add_dv['Upper_bound'] = allm_var['Upper_bound'][i]
add_dv['Bin_dec_prec'] = allm_var['Bin_dec_prec'][i]
add_dv['Steps'] = allm_var['Steps'][i]
input_add_dv = [
add_dv['Name'], add_dv['Type'], add_dv['Lower_bound'],
add_dv['Upper_bound'], add_dv['Bin_dec_prec'], add_dv['Steps']
]
input_add_dvdf = pd.DataFrame(data=[input_add_dv],
columns=[
'Name', 'Type', 'Lower_bound',
'Upper_bound', 'Bin_dec_prec',
'Steps'
])
dv = dv.append(input_add_dvdf, ignore_index=True)
input_v['range_of_decision_variables'] = dv
##Input the data into NSGA=II
nsga_2(input_v, activate_parallel, num_cores, crossover_perc,
mutation_perc)
return
def MOOSetUp():
import pandas as pd
from nsga_2 import nsga_2
allm_var = pd.DataFrame(
columns=['Name', 'Value', 'Unit', 'LB', 'UB', 'Type'])
activate_parallel = 'no'
num_cores = 4
v1 = ['x1', 10, 'm', 1, 100, 'continuous']
v2 = ['x2', 20, 'm', 1, 100, 'continuous']
t11 = pd.DataFrame(data=[v1],
columns=['Name', 'Value', 'Unit', 'LB', 'UB', 'Type'])
t12 = pd.DataFrame(data=[v2],
columns=['Name', 'Value', 'Unit', 'LB', 'UB', 'Type'])
allm_var = allm_var.append(t11, ignore_index=True)
allm_var = allm_var.append(t12, ignore_index=True)
input_v = {}
##These are parameters for the genetic algorithm to work
dim_allm_var = [2]
input_v['population'] = 100
input_v['generations'] = 50
input_v['number_of_objectives'] = 2
input_v['number_of_decision_variables'] = dim_allm_var[0]
##List of objective functions values
obj_v = pd.DataFrame(columns=['Value'])
##1
add_obj_v = {}
add_obj_v['Value'] = 0
input_add_obj_v = [add_obj_v['Value']]
input_add_obj_vdf = pd.DataFrame(data=[input_add_obj_v], columns=['Value'])
obj_v = obj_v.append(input_add_obj_vdf, ignore_index=True)
##2
add_obj_v = {}
add_obj_v['Value'] = 0
input_add_obj_v = [add_obj_v['Value']]
input_add_obj_vdf = pd.DataFrame(data=[input_add_obj_v], columns=['Value'])
obj_v = obj_v.append(input_add_obj_vdf, ignore_index=True)
input_v['obj_func_values'] = obj_v
##List of decision variables and their respective ranges
dv = pd.DataFrame(
columns=['Name', 'Initial_value', 'Unit', 'Min', 'Max', 'Type'])
for i in range(0, dim_allm_var[0]):
add_dv = {}
add_dv['Name'] = allm_var['Name'][i]
add_dv['Initial_value'] = allm_var['Value'][i]
add_dv['Unit'] = allm_var['Unit'][i]
add_dv['Min'] = allm_var['LB'][i]
add_dv['Max'] = allm_var['UB'][i]
add_dv['Type'] = allm_var['Type'][i]
input_add_dv = [
add_dv['Name'], add_dv['Initial_value'], add_dv['Unit'],
add_dv['Min'], add_dv['Max'], add_dv['Type']
]
input_add_dvdf = pd.DataFrame(
data=[input_add_dv],
columns=['Name', 'Initial_value', 'Unit', 'Min', 'Max', 'Type'])
dv = dv.append(input_add_dvdf, ignore_index=True)
input_v['range_of_decision_variables'] = dv
##Input the data into NSGA=II
nsga_2(input_v, activate_parallel, num_cores)
return