def run(n_restarts=5):
from PyGMO import algorithm, island, population
prob = earthToMars()
algo = algorithm.scipy_slsqp(max_iter=500, acc=1e-5)
# algo.screen_output = True
algo2 = algorithm.mbh(algo, n_restarts, 0.005)
algo2.screen_output = True
pop = population(prob)
pop.push_back((-3.810404036178629e-05, 8.036667366434322e-05, 0, -0.00011631957922519811, -0.0003960700040113729, 0, 0.00014343900668268246, -0.00039460589468829016, 0, 0.0004133243825183847, -0.0002676479632615287, 0, -6.353773946676955e-05, -0.0004027302771161609, 0, -0.00019483461157664088, -0.0003938299142410649, 0, 0.0003740376551173652, -0.00045439735580127933, 0, 0.00026271994456226056, -4.17413726080276e-05, 0, 0.0004025294016016401, 9.22186764465555e-05, 0, 0.0004379362102351141, -8.202101747983173e-05, 0, 2.0842990495214604e-05, -1.927554372930426e-05, 0, -2.392388475139966e-05, -6.3420840462436174e-06, 0))
pop.push_back((0.00018354551497353738, 0.0002897005581203533, 0, 9.385407683672441e-05, -0.0004375546286935724, 0, -0.00017406053466786356, -0.0004055793819144533, 0, 7.811816626063441e-05, -0.00028869842254392053, 0, 0.000280132941671916, -0.00045467528344872834, 0, 0.00031161406626870487, -0.0004418005074233615, 0, 0.00016912620000403375, -0.00045156036938030775, 0, 0.00043500734938167605, -4.4611940286304056e-05, 0, 0.00023373694896547512, 4.622353180355802e-06, 0, 0.00043504614537196785, -0.00042017445674379463, 0, 0.00016822207354911628, 0.00010574669088542543, 0, 2.1129649656070842e-05, 0.00020199652091584146, 0))
# pop.push_back((301.93091863294785, 0.0016076262089444425, 0.0015896115913838728, 0.0))
# pop.push_back((420.2372419060117, 0.010494326408284994, 0.0044382506954818565, 0.0))
# pop.push_back((411.21323621411335, 0.008748839048462907, 0.0033290148214346503, 0.0))
# pop.push_back((395.8283718212657, 0.006450877568564355, 0.002069880891910152, 0.0))
# pop.push_back((319.95400029222867, 0.0016702166037494744, 0.0013676901851197968, 0.0))
# pop.push_back((319.5113399461457, 0.00166499548529299, 0.0013736935829129556, 0.0))
# pop.push_back((320.0969905134936, 0.001671977113629641, 0.001365741362825864, 0.0))
# pop.push_back((324.8947207784664, 0.0017420256877963634, 0.0013024051696600683, 0.0))
isl = island(algo2,pop)
print("Running Monotonic Basin Hopping .... this will take a while.")
isl.evolve(1)
isl.join()
print("Is the solution found a feasible trajectory? " +
str(prob.feasibility_x(isl.population.champion.x)))
return isl.population.champion.x
def run(n_restarts=5):
from PyGMO import algorithm, island, population
prob = earthToMars()
algo = algorithm.scipy_slsqp(max_iter=500, acc=1e-5)
# algo.screen_output = True
algo2 = algorithm.mbh(algo, n_restarts, 0.005)
algo2.screen_output = True
pop = population(prob)
pop.push_back((320.33563525584435, 0.0016748335274261476, 0.0013627675495311467, 0.0))
pop.push_back((301.93091863294785, 0.0016076262089444425, 0.0015896115913838728, 0.0))
pop.push_back((301.93091863294785, 0.0016076262089444425, 0.0015896115913838728, 0.0))
pop.push_back((420.2372419060117, 0.010494326408284994, 0.0044382506954818565, 0.0))
pop.push_back((411.21323621411335, 0.008748839048462907, 0.0033290148214346503, 0.0))
pop.push_back((395.8283718212657, 0.006450877568564355, 0.002069880891910152, 0.0))
pop.push_back((319.95400029222867, 0.0016702166037494744, 0.0013676901851197968, 0.0))
pop.push_back((319.5113399461457, 0.00166499548529299, 0.0013736935829129556, 0.0))
pop.push_back((320.0969905134936, 0.001671977113629641, 0.001365741362825864, 0.0))
pop.push_back((324.8947207784664, 0.0017420256877963634, 0.0013024051696600683, 0.0))
isl = island(algo2,pop)
print("Running Monotonic Basin Hopping .... this will take a while.")
isl.evolve(1)
isl.join()
print("Is the solution found a feasible trajectory? " +
str(prob.feasibility_x(isl.population.champion.x)))
return isl.population.champion.x
def run_example3(): from PyGMO import algorithm, island prob = mga_lt_EVMe() algo = algorithm.scipy_slsqp(max_iter = 500, acc =1e-5) #algo = algorithm.snopt(major_iter=2000, opt_tol=1e-3, feas_tol=1e-9) algo2 = algorithm.mbh(algo,5,0.05) algo2.screen_output = True isl = island(algo2,prob,1) print "Running Monotonic Basin Hopping ...." isl.evolve(1); isl.join() print "Is the solution found a feasible trajectory? " + str(prob.feasibility_x(isl.population.champion.x)) prob.plot(isl.population.champion.x)
def run_example1(): from PyGMO import algorithm, island prob = mga_lt_earth_mars(nseg=15) prob.high_fidelity(True) algo = algorithm.scipy_slsqp(max_iter = 500, acc=1e-5) #algo = algorithm.snopt(major_iter=1000, opt_tol=1e-6, feas_tol=1e-11) algo2 = algorithm.mbh(algo,5,0.05) algo2.screen_output = True isl = island(algo2,prob,1) print "Running Monotonic Basin Hopping .... this will take a while" isl.evolve(1); isl.join() print "Is the solution found a feasible trajectory? " + str(prob.feasibility_x(isl.population.champion.x)) prob.plot(isl.population.champion.x)
def run_example1(n_restarts=5):
from PyGMO import algorithm, island
prob = earthToMars()
algo = algorithm.scipy_slsqp(max_iter=500, acc=1e-5)
algo2 = algorithm.mbh(algo, 5, 0.05)
algo2.screen_output = True
isl = island(algo2, prob, 1)
print("Running Monotonic Basin Hopping .... this will take a while.")
isl.evolve(1)
isl.join()
print("Is the solution found a feasible trajectory? " +
str(prob.feasibility_x(isl.population.champion.x)))
return isl.population.champion.x
def run_example4(): from PyGMO import algorithm, island,population N=20 prob = mga_lt_earth_mars_sundmann(nseg=N) algo = algorithm.scipy_slsqp(max_iter = 500, acc=1e-5) #algo = algorithm.snopt(major_iter=1000, opt_tol=1e-6, feas_tol=1e-11) algo2 = algorithm.mbh(algo,5,0.05) algo2.screen_output = True isl = island(algo2,prob,1) print "Running Monotonic Basin Hopping ...." isl.evolve(1); isl.join() print "Is the solution found a feasible trajectory? " + str(prob.feasibility_x(isl.population.champion.x)) print isl.population.champion.x prob.plot(isl.population.champion.x)
def run_example3():
from PyGMO import algorithm, island
prob = mga_lt_EVMe()
algo = algorithm.scipy_slsqp(max_iter=500, acc=1e-5)
#algo = algorithm.snopt(major_iter=500, opt_tol=1e-3, feas_tol=1e-9)
algo2 = algorithm.mbh(algo, 5, 0.05)
algo2.screen_output = True
isl = island(algo2, prob, 1)
print "Running Monotonic Basin Hopping ...."
isl.evolve(1)
isl.join()
print "Is the solution found a feasible trajectory? " + str(
prob.feasibility_x(isl.population.champion.x))
prob.plot(isl.population.champion.x)
def run_example1(n_restarts=5):
prob = mga_lt_earth_mars(nseg=15)
prob.high_fidelity(True)
algo = algorithm.scipy_slsqp(max_iter=500, acc=1e-5)
algo2 = algorithm.mbh(algo, n_restarts, 0.05)
algo2.screen_output = True
isl = island(algo2, prob, 1)
print("Running Monotonic Basin Hopping .... this will take a while.")
isl.evolve(1)
isl.join()
print("Is the solution found a feasible trajectory? " +
str(prob.feasibility_x(isl.population.champion.x)))
prob.plot(isl.population.champion.x)
def run_example4(sim,n_restarts=5):
from PyGMO import algorithm, island
prob = rocketMars1()
algo = algorithm.scipy_slsqp(max_iter=500, acc=1e-5)
# algo = algorithm.ipopt(max_iter=500, constr_viol_tol=1e-5,dual_inf_tol=1e-5,compl_inf_tol=1e-5)
# algo.screen_output = True
algo2 = algorithm.mbh(algo, n_restarts, 0.05)
algo2.screen_output = True
isl = island(algo2, prob, 1)
print("Running Monotonic Basin Hopping .... this will take a while.")
isl.evolve(1)
isl.join()
print("Is the solution found a feasible trajectory? " +
str(prob.feasibility_x(isl.population.champion.x)))
return isl.population.champion.x
def run_example1():
from PyGMO import algorithm, island
prob = mga_lt_earth_mars(nseg=15)
prob.high_fidelity(True)
algo = algorithm.scipy_slsqp(max_iter=500, acc=1e-5)
#algo = algorithm.snopt(major_iter=1000, opt_tol=1e-6, feas_tol=1e-11)
algo2 = algorithm.mbh(algo, 5, 0.05)
algo2.screen_output = True
isl = island(algo2, prob, 1)
print "Running Monotonic Basin Hopping .... this will take a while"
isl.evolve(1)
isl.join()
print "Is the solution found a feasible trajectory? " + str(
prob.feasibility_x(isl.population.champion.x))
prob.plot(isl.population.champion.x)
def run_example4():
from PyGMO import algorithm, island, population
N = 20
prob = mga_lt_earth_mars_sundmann(nseg=N)
algo = algorithm.scipy_slsqp(max_iter=500, acc=1e-5)
# algo = algorithm.snopt(major_iter=1000, opt_tol=1e-6, feas_tol=1e-11)
algo2 = algorithm.mbh(algo, 5, 0.05)
algo2.screen_output = True
isl = island(algo2, prob, 1)
print("Running Monotonic Basin Hopping ....")
isl.evolve(1)
isl.join()
print("Is the solution found a feasible trajectory? " +
str(prob.feasibility_x(isl.population.champion.x)))
print(isl.population.champion.x)
prob.plot(isl.population.champion.x)
def check_low_thrustable(inci, verbose=False): """ Given a incipit_stats object, checks each leg on los_thrustabality. It will return a list of tuple, one tuple for each leg. Each tuple contains (feasibility, famous coefficient).""" if os.name == 'posix': alg = algorithm.snopt(500, feas_tol=1e-9, opt_tol=1e-2, screen_output=False) else: alg = algorithm.scipy_slsqp(screen_output=False) prob = one_lt_leg(t = inci.initial[0], r=inci.initial[1], v=inci.initial[2], high_fidelity=True) pop = population(prob, 1) pop = alg.evolve(pop) feasible = prob.feasibility_x(pop.champion.x) feas_list = [ feasible] fam_coeff_list = [ (inci.DV[0] / (inci.T[0]*DAY2SEC)) / (0.1 / 2000.0) ] if verbose: print '+++ Checking feasibility of: ' + inci.filename + ' +++' print 'Initial leg feasible? --> ' + str(prob.feasibility_x(pop.champion.x)) for i in xrange(1, inci.n_legs): prob = one_lt_leg(t = [inci.t_P[i-1], inci.t_P[i]], r=[inci.r_P[i-1], inci.r_P[i]], v=[inci.v_sc[i-1][0], inci.v_sc[i-1][1]], high_fidelity=True) pop = population(prob, 1) pop = alg.evolve(pop) feasible = feasible and prob.feasibility_x(pop.champion.x) if verbose: print 'Leg no. ' + str(i) + ' feasible? --> ' + str(prob.feasibility_x(pop.champion.x)) # compute famous coefficient fam_coeff_list.append( (inci.DV[i] / (inci.T[i]*DAY2SEC)) / (0.1 / 2000.0) ) feas_list.append(prob.feasibility_x(pop.champion.x)) if verbose: if feasible: print 'Feasible!' else: print 'infeasible...' return zip(feas_list, fam_coeff_list)
def run_example6(n_seg=5):
"""
This example demonstrates the optimization of a multiple randezvous mission (low-thrust).
Such a mission (including more asteroids) is also called asteroid hopping
The spacecraft performances, as well as the three asteroids visited, are taken from the GTOC7 problem description.
"""
from PyGMO import algorithm, population
from PyKEP.trajopt import mr_lt_nep
from PyKEP import planet_gtoc7
algo = algorithm.scipy_slsqp(max_iter=500, acc=1e-5, screen_output=True)
prob = mr_lt_nep(t0=[9600., 9700.],
seq=[
planet_gtoc7(5318),
planet_gtoc7(14254),
planet_gtoc7(7422),
planet_gtoc7(5028)
],
n_seg=n_seg,
mass=[800., 2000.],
leg_tof=[100., 365.25],
rest=[30., 365.25],
Tmax=0.3,
Isp=3000.,
traj_tof=365.25 * 3.,
objective='mass',
c_tol=1e-05)
pop = population(prob, 1)
pop = algo.evolve(pop)
solution = pop.champion.x
if prob.feasibility_x(solution):
print "FEASIBILE!!!"
ax = prob.plot(solution)
else:
print "INFEASIBLE :("
ax = None
return prob, solution, ax
def run_example6(n_seg=5):
"""
This example demonstrates the optimization of a multiple randezvous mission (low-thrust).
Such a mission (including more asteroids) is also called asteroid hopping
The spacecraft performances, as well as the three asteroids visited, are taken from the GTOC7 problem description.
"""
from PyGMO import algorithm, population
from PyKEP.trajopt import mr_lt_nep
from PyKEP.planet import gtoc7
algo = algorithm.scipy_slsqp(max_iter=500, acc=1e-5, screen_output=True)
prob = mr_lt_nep(
t0=[9600., 9700.],
seq=[gtoc7(5318), gtoc7(14254), gtoc7(7422), gtoc7(5028)],
n_seg=n_seg,
mass=[800., 2000.],
leg_tof=[100., 365.25],
rest=[30., 365.25],
Tmax=0.3,
Isp=3000.,
traj_tof=365.25 * 3.,
objective='mass',
c_tol=1e-05
)
pop = population(prob, 1)
pop = algo.evolve(pop)
solution = pop.champion.x
if prob.feasibility_x(solution):
print("FEASIBILE!!!")
ax = prob.plot(solution)
else:
print("INFEASIBLE :(")
ax = None
return prob, solution, ax
0.35180899558417034, 1.6036558613069618)
print(x)
print('-' * 50)
trajs = int(sys.argv[1])
n_th = 1
if len(sys.argv) > 2:
n_th = int(sys.argv[2])
screen_output = False
snopt_algo = algorithm.snopt(400,
opt_tol=1e-3,
feas_tol=1e-6,
screen_output=screen_output)
sci_algo = algorithm.scipy_slsqp(max_iter=30,
acc=1E-8,
epsilon=1.49e-08,
screen_output=screen_output)
if len(sys.argv) != 4 or sys.argv[3] == 'simple':
run_multithread(landing_problem,
trajs,
n_th,
initial_bounds,
'data/' + filedir,
display=True,
algo=sci_algo)
elif sys.argv[3] == 'simple_qc':
run_multithread(landing_problem,
trajs,
n_th,
initial_bounds,
walk_bounds = [x0b, y0b, vx0b, vy0b, theta0, omega0, m0b]
filedir = 'falcon'
x = (0.0004063623657985068, -0.015166893186127163, 0.00047398741968363283, -0.0815609687198395, 0.0005701909839526759,
0.00020132959190827737, 0.35180899558417034, 1.6036558613069618)
print(x)
print('-'*50)
trajs = int(sys.argv[1])
n_th = 1
if len(sys.argv) > 2:
n_th = int(sys.argv[2])
screen_output = False
snopt_algo = algorithm.snopt(400, opt_tol=1e-3, feas_tol=1e-6,
screen_output=screen_output)
sci_algo = algorithm.scipy_slsqp(max_iter=30, acc=1E-8, epsilon=1.49e-08,
screen_output=screen_output)
if len(sys.argv) != 4 or sys.argv[3] == 'simple':
run_multithread(landing_problem, trajs, n_th, initial_bounds, 'data/' +
filedir, display=True, algo=sci_algo)
elif sys.argv[3] == 'simple_qc':
run_multithread(landing_problem, trajs, n_th, initial_bounds, 'data/' +
filedir, display=True, qc=True, algo=sci_algo, state_step=0.02)
elif sys.argv[3] == 'rw_qc':
run_multithread(landing_problem, trajs, n_th, initial_bounds, 'data/' +
filedir, display=True, qc=True)
elif sys.argv[3] == 'falcon_qc':
print(initial_bounds)
run_multithread(landing_problem, trajs, n_th, initial_bounds, 'data/' +
filedir, display=True, qc=True, initial_random_walk=x,
