def main():
problem = setup()
## Base Input Values
#output = problem.objective()
## Uncomment to view contours of the design space
#variable_sweep(problem)
## Uncomment for the first optimization
output = scipy_setup.SciPy_Solve(problem,solver='SLSQP')
print (output)
## Uncomment these lines when you want to start an optimization problem from a different initial guess
#inputs = [1.28, 1.38]
#scaling = problem.optimization_problem.inputs[:,3] #have to rescale inputs to start problem from here
#scaled_inputs = np.multiply(inputs,scaling)
#problem.optimization_problem.inputs[:,1] = scaled_inputs
#output = scipy_setup.SciPy_Solve(problem,solver='SLSQP')
#print output
print('fuel burn = ', problem.summary.base_mission_fuelburn)
print('fuel margin = ', problem.all_constraints())
Plot_Mission.plot_mission(problem)
return
def main():
problem = setup()
output = scipy_setup.SciPy_Solve(problem)
problem.translate(output)
Plot_Mission.plot_mission(problem.results.mission)
return
def main():
problem = setup()
output = scipy_setup.SciPy_Solve(problem, sense_step=0.0005)
## Uncomment these lines when you want to start an optimization problem from a different initial guess
#inputs = [1.28, 1.38]
#scaling = problem.optimization_problem.inputs[:,3] #have to rescale inputs to start problem from here
#scaled_inputs = np.multiply(inputs,scaling)
#problem.optimization_problem.inputs[:,1] = scaled_inputs
#output = scipy_setup.SciPy_Solve(problem,solver='SLSQP')
problem.translate(output)
Plot_Mission.plot_mission(problem)
return
def main():
problem = setup()
## Base Input Values
output = problem.objective()
# Uncomment to view contours of the design space
#variable_sweep(problem)
# Uncomment for the first optimization
output = scipy_setup.SciPy_Solve(problem, solver='SLSQP')
print(output)
print('fuel burn = ', problem.summary.base_mission_fuelburn)
print('fuel margin = ', problem.all_constraints())
Plot_Mission.plot_mission(problem)
return
def main():
problem = setup()
output = scipy_setup.SciPy_Solve(problem)
print output
## Uncomment to view contours of the design space
variable_sweep(problem)
## Uncomment these lines when you want to start an optimization problem from a different initial guess
#inputs = [1.28, 1.38]
#scaling = problem.optimization_problem.inputs[:,3] #have to rescale inputs to start problem from here
#scaled_inputs = np.multiply(inputs,scaling)
#problem.optimization_problem.inputs[:,1] = scaled_inputs
#output = scipy_setup.SciPy_Solve(problem,solver='SLSQP')
problem.translate(output)
Plot_Mission.plot_mission(problem.results.mission)
return
def main():
t0 = time.time()
problem = setup()
## Base Input Values
output = problem.objective()
print output
Plot_Mission.plot_mission(problem, -1)
# # Uncomment to view contours of the design space
# variable_sweep(problem)
print ' '
print ' Initial Guess Results '
print ' '
print 'Fuel Burn = ', float(problem.summary.base_mission_fuelburn)
print 'Cruise Fuel = ', float(problem.summary.cruise_fuel)
print 'Block Fuel = ', float(problem.summary.block_fuel)
print 'MTOW = ', float(problem.summary.MTOW)
print 'BOW = ', float(problem.summary.BOW)
print 'TOFL = ', float(problem.summary.takeoff_field_length)
print 'GRAD = ', float(
problem.summary.second_segment_climb_gradient_takeoff)
print 'Cruise Alt = ', float(problem.summary.cruise_altitude)
print 'Design Ran = ', float(problem.summary.design_range)
print 'Cruise Ran = ', float(problem.summary.cruise_range)
print 'Total Ran = ', float(problem.summary.total_range)
print 'Time To Cli = ', float(problem.summary.time_to_climb_value)
print 'TOW HH = ', float(problem.summary.TOW_HH)
print 'Fuel HH = ', float(problem.summary.FUEL_HH)
print ' '
print 'Constraints = ', problem.all_constraints()
print ' '
# ------------------------------------------------------------------
# Pareto
fuelburn = []
allconstraints = []
MTOW = []
finalvalue = []
grad = []
tofl = []
# betas = [1., 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1, 0.]
# betas = [1., 0.75, 0.5, 0.25, 0.]
betas = [0.]
# fid = open('Pareto_results.txt', 'w') # Open output file
# fid.write(' Pareto results \n')
# fid.close()
for i, beta in enumerate(betas):
fid = open('Pareto_results.txt', 'ab') # Open output file
fid.write('Pareto Frontier. Run number: ' + str(i) + ' \n')
print('Pareto Frontier. Run number: ' + str(i))
# updating Beta value
design_vars = problem.optimization_problem.inputs[:, 1]
design_vars[-1] = beta
design_vars[0] = 70.43381
design_vars[1] = 9.613779
design_vars[2] = 0.12272157
design_vars[3] = 24.391733
design_vars[4] = 1106.5334
problem.optimization_problem.inputs[:, 1] = design_vars
bounds = problem.optimization_problem.inputs[:, 2]
bounds[-1] = list(bounds[-1])
bounds[-1][0] = beta
bounds[-1][1] = beta
bounds[-1] = tuple(bounds[-1])
problem.optimization_problem.inputs[:, 2] = bounds
output = scipy_setup.SciPy_Solve(problem, solver='SLSQP')
print output
print ' '
print ' Final Results '
print ' '
print 'Fuel Burn = ', float(problem.summary.base_mission_fuelburn)
print 'Cruise Fuel = ', float(problem.summary.cruise_fuel)
print 'Block Fuel = ', float(problem.summary.block_fuel)
print 'MTOW = ', float(problem.summary.MTOW)
print 'BOW = ', float(problem.summary.BOW)
print 'TOFL = ', float(problem.summary.takeoff_field_length)
print 'GRAD = ', float(
problem.summary.second_segment_climb_gradient_takeoff)
print 'Cruise Alt = ', float(problem.summary.cruise_altitude)
print 'Design Ran = ', float(problem.summary.design_range)
print 'Cruise Ran = ', float(problem.summary.cruise_range)
print 'Total Ran = ', float(problem.summary.total_range)
print 'Time To Cli = ', float(problem.summary.time_to_climb_value)
print 'TOW HH = ', float(problem.summary.TOW_HH)
print 'Fuel HH = ', float(problem.summary.FUEL_HH)
print ' '
print 'Constraints = ', problem.all_constraints()
Plot_Mission.plot_mission(problem, i)
finalvalue.append(output)
fuelburn.append(problem.summary.base_mission_fuelburn)
allconstraints.append(problem.all_constraints())
grad.append(problem.summary.second_segment_climb_gradient_takeoff)
tofl.append(problem.summary.takeoff_field_length)
MTOW.append(problem.summary.MTOW)
fid.write(str(fuelburn[-1]) + ' \n')
fid.write(str(grad[-1]) + ' \n')
fid.write(str(tofl[-1]) + ' \n')
fid.write(str(MTOW[-1]) + ' \n')
fid.write(str(allconstraints[-1]) + ' \n')
fid.write(str(finalvalue[-1]) + ' \n')
fid.write('\n \n')
fid.close()
fid = open('Pareto_results.txt', 'ab') # Open output file
elapsed = time.time() - t0
fid.write('Total run time: ' + str(elapsed))
print('Total run time: ' + str(elapsed))
fid.close()
return
def main():
t0 = time.time()
problem = setup()
## Base Input Values
output = problem.objective()
print output
Plot_Mission.plot_mission(problem, -1)
# # Uncomment to view contours of the design space
# variable_sweep(problem)
print ' '
print ' Initial Guess Results '
print ' '
print 'Fuel Burn = ', float(problem.summary.base_mission_fuelburn)
print 'Cruise Fuel = ', float(problem.summary.cruise_fuel)
print 'Block Fuel = ', float(problem.summary.block_fuel)
print 'MTOW = ', float(problem.summary.MTOW)
print 'BOW = ', float(problem.summary.BOW)
print 'TOFL = ', float(problem.summary.takeoff_field_length)
print 'GRAD = ', float(problem.summary.second_segment_climb_gradient_takeoff)
print 'Cruise Alt = ', float(problem.summary.cruise_altitude)
print 'Design Ran = ', float(problem.summary.design_range)
print 'Cruise Ran = ', float(problem.summary.cruise_range)
print 'Total Ran = ', float(problem.summary.total_range)
print 'Time To Cli = ', float(problem.summary.time_to_climb_value)
print 'TOW HH = ', float(problem.summary.TOW_HH)
print 'Fuel HH = ', float(problem.summary.FUEL_HH)
print ' '
print 'Constraints = ', problem.all_constraints()
print ' '
last_inputs = problem.last_inputs[:, 1]
print 'S = ', last_inputs[0]
print 'AR = ', last_inputs[1]
print 't/c = ', last_inputs[2]
print 'Sweep Ang = ', 3
# ------------------------------------------------------------------
# Pareto
fuelburn = []
allconstraints = []
MTOW = []
finalvalue = []
grad = []
tofl = []
betas = [0., 0.25, 0.5, 0.75, 1.]
fid = open('Pareto_results.txt', 'w') # Open output file
fid.write(' Pareto results \n')
fid.close()
for i, beta in enumerate(betas):
fid = open('Pareto_results.txt', 'ab') # Open output file
fid.write('Pareto Frontier. Run number: ' + str(i) + ' \n')
print('Pareto Frontier. Run number: ' + str(i))
# updating Beta value
design_vars = problem.optimization_problem.inputs[:, 1]
problem.beta = beta
design_vars[0] = 61.
design_vars[1] = 12
design_vars[2] = 0.15
problem.optimization_problem.inputs[:, 1] = design_vars
output = scipy_setup.SciPy_Solve(problem, solver='SLSQP', sense_step=1.4901161193847656e-05)
# output = pyopt_setup.Pyopt_Solve(problem, solver='ALPSO')
finalvalue.append(output)
print output
print ' '
print ' Final Results '
print ' '
print 'Fuel Burn = ', float(problem.summary.base_mission_fuelburn)
print 'Cruise Fuel = ', float(problem.summary.cruise_fuel)
print 'Block Fuel = ', float(problem.summary.block_fuel)
print 'MTOW = ', float(problem.summary.MTOW)
print 'BOW = ', float(problem.summary.BOW)
print 'TOFL = ', float(problem.summary.takeoff_field_length)
print 'GRAD = ', float(problem.summary.second_segment_climb_gradient_takeoff)
print 'Cruise Alt = ', float(problem.summary.cruise_altitude)
print 'Design Ran = ', float(problem.summary.design_range)
print 'Cruise Ran = ', float(problem.summary.cruise_range)
print 'Total Ran = ', float(problem.summary.total_range)
print 'Time To Cli = ', float(problem.summary.time_to_climb_value)
print 'TOW HH = ', float(problem.summary.TOW_HH)
print 'Fuel HH = ', float(problem.summary.FUEL_HH)
print ' '
print 'Constraints = ', problem.all_constraints()
print ' '
last_inputs = finalvalue[-1]
print 'S = ', last_inputs[0] * 61.
print 'AR = ', last_inputs[1] * 12.
print 't/c = ', last_inputs[2] * 0.15
print 'Sweep Ang = ', 3
# -----------------------------------------
Plot_Mission.plot_mission(problem, i)
fuelburn.append(problem.summary.base_mission_fuelburn)
allconstraints.append(problem.all_constraints())
grad.append(problem.summary.second_segment_climb_gradient_takeoff)
tofl.append(problem.summary.takeoff_field_length)
MTOW.append(problem.summary.MTOW)
fid.write(str(fuelburn[-1]) + ' \n')
fid.write(str(grad[-1]) + ' \n')
fid.write(str(tofl[-1]) + ' \n')
fid.write(str(MTOW[-1]) + ' \n')
fid.write(str(allconstraints[-1]) + ' \n')
fid.write(str(finalvalue[-1]) + ' \n')
fid.write('\n \n')
fid.close()
fid = open('Pareto_results.txt', 'ab') # Open output file
elapsed = time.time() - t0
fid.write('Total run time: ' + str(elapsed))
print('Total run time: ' + str(elapsed))
fid.close()
return
def main():
seed = np.random.seed(1)
# ------------------------------------------------------------------
# SLSQP
# ------------------------------------------------------------------
solver_name = 'SLSQP'
problem = setup(solver_name)
problem.optimization_problem.constraints = np.array([
['x1', '>', -10., 1., Units.less],
['x1', '=', 0., 1., Units.less],
['x2', '>', 1., 1., Units.less],
['x2', '<', 2., 1., Units.less],
])
print('\n\n Checking basic additive with one active constraint...')
# suppress iteration printout
Nexus.translate(problem)
sys.stdout = open(os.devnull, 'w')
outputs = scipy_setup.SciPy_Solve(problem,
solver='SLSQP',
sense_step=1.4901161193847656e-08,
pop_size=10,
prob_seed=seed)
# end suppression of interation printout
sys.stdout = sys.__stdout__
print(outputs)
obj = scipy_setup.SciPy_Problem(problem, outputs)[0]
x1 = outputs[0]
x2 = outputs[1]
# Check Results
assert (np.isclose(obj, 1, atol=1e-6))
assert (np.isclose(x1, 0, atol=1e-2))
assert (np.isclose(x2, 1, atol=1e-2))
# test writing output function
filename = 'optimization_output.txt'
write_optimization_outputs(problem, filename)
os.remove('optimization_output.txt')
# ------------------------------------------------------------------
# Differential Evolution
# ------------------------------------------------------------------
print('\n\n Checking differential evolution algorithm')
solver_name = 'differential_evolution'
problem = setup(solver_name)
problem.optimization_problem.constraints = np.array([
['x1', '>', -10., 1., Units.less],
['x1', '=', 0., 1., Units.less],
['x2', '>', 1., 1., Units.less],
['x2', '<', 2., 1., Units.less],
])
# suppress iteration printout
sys.stdout = open(os.devnull, 'w')
outputs = scipy_setup.SciPy_Solve(problem,
solver='differential_evolution',
sense_step=1.4901161193847656e-08,
pop_size=10,
prob_seed=seed)
# end suppression of interation printout
sys.stdout = sys.__stdout__
print(outputs)
obj = outputs.fun
x1 = outputs.x[0]
x2 = outputs.x[1]
# Check Results
assert (np.isclose(obj, 1, atol=1e-5))
assert (np.isclose(x1, 0, atol=1e-4))
assert (np.isclose(x2, 1, atol=1e-4))
# ------------------------------------------------------------------
# Particle Swarm Optimization
# ------------------------------------------------------------------
solver_name = 'particle_swarm_optimization'
problem = setup(solver_name)
problem.optimization_problem.constraints = np.array([
['x1', '>', -10., 1., Units.less],
['x2', '>', 1., 1., Units.less],
['x2', '<', 2., 1., Units.less],
])
print('\n\n Checking particle swarm optimization algorithm')
# suppress iteration printout
sys.stdout = open(os.devnull, 'w')
outputs = scipy_setup.SciPy_Solve(problem,
solver='particle_swarm_optimization',
sense_step=1.4901161193847656e-08,
pop_size=100,
prob_seed=seed)
# end suppression of interation printout
sys.stdout = sys.__stdout__
print(outputs)
obj = outputs[1][0]
x1 = outputs[0][0]
x2 = outputs[0][1]
# Check Results
assert (np.isclose(obj, 1, atol=1e-2))
assert (np.isclose(x1, 0, atol=1e-1))
assert (np.isclose(x2, 1, atol=1e-1))
return
def main():
t0 = time.time()
problem = setup()
## Base Input Values
output = problem.objective()
print output
Plot_Mission.plot_mission(problem, -1)
# # Uncomment to view contours of the design space
# variable_sweep(problem)
print ' '
print ' Initial Guess Results '
print ' '
print 'Fuel Burn = ', float(problem.summary.base_mission_fuelburn)
print 'Cruise Fuel = ', float(problem.summary.cruise_fuel)
print 'Block Fuel = ', float(problem.summary.block_fuel)
print 'MTOW = ', float(problem.summary.MTOW)
print 'BOW = ', float(problem.summary.BOW)
print 'TOFL = ', float(problem.summary.takeoff_field_length)
print 'GRAD = ', float(
problem.summary.second_segment_climb_gradient_takeoff)
print 'Cruise Alt = ', float(problem.summary.cruise_altitude)
print 'Design Ran = ', float(problem.summary.design_range)
print 'Cruise Ran = ', float(problem.summary.cruise_range)
print 'Total Ran = ', float(problem.summary.total_range)
print 'Time To Cli = ', float(problem.summary.time_to_climb_value)
print 'TOW HH = ', float(problem.summary.TOW_HH)
print 'Fuel HH = ', float(problem.summary.FUEL_HH)
print ' '
print 'Constraints = ', problem.all_constraints()
print ' '
last_inputs = problem.last_inputs[:, 1]
print 'S = ', last_inputs[0]
print 'AR = ', last_inputs[1]
print 't/c = ', last_inputs[2]
print 'Sweep Ang = ', last_inputs[3]
# ------------------------------------------------------------------
# Pareto
fuelburn = []
allconstraints = []
MTOW = []
finalvalue = []
grad = []
tofl = []
variations = []
fid = open('DoE_results.txt', 'w') # Open output file
fid.write(' DoE results \n')
fid.close()
deltas = [0.95, 1.05]
original_values = deepcopy(problem.optimization_problem.inputs[:, 1])
original_bounds = deepcopy(problem.optimization_problem.inputs[:, 2])
for k, delta in enumerate(deltas):
for i in range(len(problem.optimization_problem.inputs) - 2):
fid = open('DoE_results.txt', 'ab') # Open output file
fid.write('DoE - parameter study. Run number: ' +
str(i +
(len(problem.optimization_problem.inputs) - 2) * k) +
' \n')
print('DoE - parameter study. Run number: ' +
str(i + (len(problem.optimization_problem.inputs) - 2) * k))
inputs = [1., 1., 1., 1., 1., 1., 1.]
inputs[i] = delta
variations.append(inputs)
print('Scaling Inputs: ' + str(inputs))
# reset values
problem.optimization_problem.inputs[:,
1] = deepcopy(original_values)
problem.optimization_problem.inputs[:,
2] = deepcopy(original_bounds)
# changing parameters values
scaling = problem.optimization_problem.inputs[:, 1]
scaled_inputs = np.multiply(inputs, scaling)
problem.optimization_problem.inputs[:, 1] = scaled_inputs
# changing parameters bounds
bounds = problem.optimization_problem.inputs[:, 2]
bounds[i] = list(bounds[i])
bounds[i][0] = bounds[i][0] * inputs[i]
bounds[i][1] = bounds[i][1] * inputs[i]
bounds[i] = tuple(bounds[i])
problem.optimization_problem.inputs[:, 2] = bounds
output = scipy_setup.SciPy_Solve(problem, solver='SLSQP')
print output
finalvalue.append(output)
print ' '
print ' Final Results '
print ' '
print 'Fuel Burn = ', float(
problem.summary.base_mission_fuelburn)
print 'Cruise Fuel = ', float(problem.summary.cruise_fuel)
print 'Block Fuel = ', float(problem.summary.block_fuel)
print 'MTOW = ', float(problem.summary.MTOW)
print 'BOW = ', float(problem.summary.BOW)
print 'TOFL = ', float(problem.summary.takeoff_field_length)
print 'GRAD = ', float(
problem.summary.second_segment_climb_gradient_takeoff)
print 'Cruise Alt = ', float(problem.summary.cruise_altitude)
print 'Design Ran = ', float(problem.summary.design_range)
print 'Cruise Ran = ', float(problem.summary.cruise_range)
print 'Total Ran = ', float(problem.summary.total_range)
print 'Time To Cli = ', float(problem.summary.time_to_climb_value)
print 'TOW HH = ', float(problem.summary.TOW_HH)
print 'Fuel HH = ', float(problem.summary.FUEL_HH)
print ' '
print 'Constraints = ', problem.all_constraints()
last_inputs = finalvalue[-1]
print 'S = ', last_inputs[0]
print 'AR = ', last_inputs[1]
print 't/c = ', last_inputs[2]
print 'Sweep Ang = ', last_inputs[3]
Plot_Mission.plot_mission(
problem,
i + (len(problem.optimization_problem.inputs) - 2) * k)
fuelburn.append(problem.summary.base_mission_fuelburn)
allconstraints.append(problem.all_constraints())
grad.append(problem.summary.second_segment_climb_gradient_takeoff)
tofl.append(problem.summary.takeoff_field_length)
MTOW.append(problem.summary.MTOW)
fid.write(str(fuelburn[-1]) + ' \n')
fid.write(str(grad[-1]) + ' \n')
fid.write(str(tofl[-1]) + ' \n')
fid.write(str(MTOW[-1]) + ' \n')
fid.write(str(allconstraints[-1]) + ' \n')
fid.write(str(variations[-1]) + ' \n')
fid.write(str(finalvalue[-1]) + ' \n')
fid.write('\n \n')
fid.close()
fid = open('DoE_results.txt', 'ab') # Open output file
elapsed = time.time() - t0
fid.write('Total run time: ' + str(elapsed))
print('Total run time: ' + str(elapsed))
fid.close()
return
def main():
problem = setup()
## Base Input Values
output = problem.objective()
# # Uncomment to view contours of the design space
# variable_sweep(problem)
Plot_Mission.plot_mission(problem, -1)
# ------------------------------------------------------------------
# DoE
fuelburn = []
allconstraints = []
variations = []
finalvalue = []
grad = []
tofl = []
fid = open('DoE_results.txt', 'w') # Open output file
fid.write(' DoE results \n')
deltas = [0.95, 1.05]
original_values = deepcopy(problem.optimization_problem.inputs[:, 1])
original_bounds = deepcopy(problem.optimization_problem.inputs[:, 2])
for k, delta in enumerate(deltas):
for i in range(len(problem.optimization_problem.inputs)-1):
fid.write('DoE - parameter study. Run number: '+str(i+(len(problem.optimization_problem.inputs)-1)*k)+' \n')
print ('DoE - parameter study. Run number: '+str(i+(len(problem.optimization_problem.inputs)-1)*k))
inputs = [1., 1., 1., 1., 1., 1.]
inputs[i] = delta
variations.append(inputs)
print ('Scaling Inputs: '+str(inputs))
# reset values
problem.optimization_problem.inputs[:, 1] = deepcopy(original_values)
problem.optimization_problem.inputs[:, 2] = deepcopy(original_bounds)
# changing parameters values
scaling = problem.optimization_problem.inputs[:, 1]
scaled_inputs = np.multiply(inputs, scaling)
problem.optimization_problem.inputs[:, 1] = scaled_inputs
# changing parameters bounds
bounds = problem.optimization_problem.inputs[:, 2]
bounds[i] = list(bounds[i])
bounds[i][0] = bounds[i][0] * inputs[i]
bounds[i][1] = bounds[i][1] * inputs[i]
bounds[i] = tuple(bounds[i])
problem.optimization_problem.inputs[:, 2] = bounds
output = scipy_setup.SciPy_Solve(problem, solver='SLSQP')
print output
finalvalue.append(output)
fuelburn.append(problem.summary.base_mission_fuelburn)
allconstraints.append(problem.all_constraints())
grad.append(problem.summary.second_segment_climb_gradient_takeoff)
tofl.append(problem.summary.takeoff_field_length)
print 'fuel burn = ', problem.summary.base_mission_fuelburn
print 'Constraints = ', problem.all_constraints()
Plot_Mission.plot_mission(problem, i+(len(problem.optimization_problem.inputs)-1)*k)
print ''
fid.write(str(fuelburn[-1])+' \n')
fid.write(str(grad[-1]) + ' \n')
fid.write(str(tofl[-1]) + ' \n')
fid.write(str(allconstraints[-1]) + ' \n')
fid.write(str(variations[-1]) + ' \n')
fid.write(str(finalvalue[-1]) + ' \n')
fid.write('\n \n')
fid.close()
# Plot_Mission.plot_mission(problem, 0)
return