In [1]:
# Import necessary resources
import sys
sys.path.append('../')
from numpy import *
from PyGMO import *
from Trajectory import Point_Lander
from Optimisation import Hermite_Simpson_Seperated
import matplotlib.pyplot as plt
In [2]:
# Instantiate model and problem
model = Point_Lander()
prob = Hermite_Simpson_Seperated(model, nsegs=20)
In [3]:
# Generate ballistic guess with 20 segments
zguess = prob.Guess.Ballistic(tf=25)
# Decode the guess
tf, sb, cb, s, c = prob.Decode(zguess)
# Visualise the guess
plt.plot(s[:,0], s[:,1], 'k.-')
plt.show()
In [4]:
# Specify the algorithm to be used
algo = algorithm.scipy_slsqp(max_iter=5000, screen_output=True)
algoh = algorithm.mbh(algo, stop=1, screen_output=True)
# Create an empty population
pop = population(prob)
# Add the ballistic guess
pop.push_back(zguess)
In [5]:
# Optimise the trajectory
pop = algoh.evolve(pop)
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559 185167 -9.244030E+03 1.000000E+00
560 185499 -9.244030E+03 1.000000E+00
561 185831 -9.244030E+03 1.000000E+00
562 186163 -9.244030E+03 1.000000E+00
563 186495 -9.244030E+03 1.000000E+00
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569 188488 -9.244030E+03 1.000000E+00
570 188820 -9.244030E+03 1.000000E+00
571 189152 -9.244030E+03 1.000000E+00
572 189485 -9.244030E+03 1.000000E+00
573 189818 -9.244030E+03 1.000000E+00
574 190151 -9.244030E+03 1.000000E+00
575 190484 -9.244030E+03 1.000000E+00
576 190817 -9.244030E+03 1.000000E+00
577 191150 -9.244030E+03 1.000000E+00
578 191483 -9.244030E+03 1.000000E+00
579 191816 -9.244030E+03 1.000000E+00
580 192149 -9.244030E+03 1.000000E+00
581 192482 -9.244030E+03 1.000000E+00
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583 193148 -9.244030E+03 1.000000E+00
584 193480 -9.244030E+03 1.000000E+00
585 193812 -9.244030E+03 1.000000E+00
586 194144 -9.244030E+03 1.000000E+00
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588 194808 -9.244030E+03 1.000000E+00
589 195140 -9.244030E+03 1.000000E+00
590 195472 -9.244030E+03 1.000000E+00
591 195804 -9.244030E+03 1.000000E+00
592 196135 -9.244030E+03 1.000000E+00
Optimization terminated successfully. (Exit mode 0)
Current function value: -9244.02974805
Iterations: 592
Function evaluations: 196136
Gradient evaluations: 592
NIT FC OBJFUN GNORM
1 331 -9.546458E+03 1.000000E+00
2 662 -9.229312E+03 1.000000E+00
3 993 -9.214920E+03 1.000000E+00
4 1324 -9.213998E+03 1.000000E+00
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204 67526 -9.243148E+03 1.000000E+00
205 67857 -9.243148E+03 1.000000E+00
206 68188 -9.243148E+03 1.000000E+00
207 68519 -9.243148E+03 1.000000E+00
208 68850 -9.243148E+03 1.000000E+00
209 69181 -9.243148E+03 1.000000E+00
210 69512 -9.243148E+03 1.000000E+00
211 69843 -9.243148E+03 1.000000E+00
212 70174 -9.243148E+03 1.000000E+00
213 70505 -9.243148E+03 1.000000E+00
214 70836 -9.243148E+03 1.000000E+00
215 71167 -9.243148E+03 1.000000E+00
216 71498 -9.243148E+03 1.000000E+00
217 71829 -9.243148E+03 1.000000E+00
218 72160 -9.243148E+03 1.000000E+00
219 72491 -9.243148E+03 1.000000E+00
220 72822 -9.243148E+03 1.000000E+00
221 73153 -9.243148E+03 1.000000E+00
222 73485 -9.243148E+03 1.000000E+00
223 73817 -9.243148E+03 1.000000E+00
224 74149 -9.243148E+03 1.000000E+00
225 74480 -9.243148E+03 1.000000E+00
226 74811 -9.243148E+03 1.000000E+00
227 75142 -9.243148E+03 1.000000E+00
228 75473 -9.243148E+03 1.000000E+00
229 75804 -9.243148E+03 1.000000E+00
230 76135 -9.243148E+03 1.000000E+00
231 76466 -9.243148E+03 1.000000E+00
232 76797 -9.243148E+03 1.000000E+00
233 77128 -9.243148E+03 1.000000E+00
Optimization terminated successfully. (Exit mode 0)
Current function value: -9243.14782684
Iterations: 233
Function evaluations: 77129
Gradient evaluations: 233
In [6]:
# Visualise the solution
z = pop.champion.x
tf, sb, cb, s, c = prob.Decode(z)
plt.plot(s[:,0], s[:,1], 'k.-')
plt.show()
In [9]:
# Save the solution
save('../Data/Hermite_Simpson_Seperated/20Seg/HSS_20_Base', z)
In [10]:
# Now with 40 segs
model = Point_Lander()
prob = Hermite_Simpson_Seperated(model, nsegs=40)
In [11]:
# Generate ballistic guess with 20 segments
zguess = prob.Guess.Ballistic(tf=25)
# Decode the guess
tf, sb, cb, s, c = prob.Decode(zguess)
# Visualise the guess
plt.plot(s[:,0], s[:,1], 'k.-')
plt.show()
In [12]:
# Specify the algorithm to be used
algo = algorithm.scipy_slsqp(max_iter=5000, screen_output=True)
algoh = algorithm.mbh(algo, stop=1, screen_output=True)
# Create an empty population
pop = population(prob)
# Add the ballistic guess
pop.push_back(zguess)
In [ ]:
# Optimise the trajectory
pop = algoh.evolve(pop)
NIT FC OBJFUN GNORM
1 651 -9.917885E+03 1.000000E+00
2 1302 -9.918265E+03 1.000000E+00
3 1953 -9.918533E+03 1.000000E+00
4 2604 -9.917776E+03 1.000000E+00
5 3255 -9.916760E+03 1.000000E+00
6 3906 -9.914036E+03 1.000000E+00
7 4557 -9.910576E+03 1.000000E+00
8 5208 -9.903735E+03 1.000000E+00
9 5859 -9.895696E+03 1.000000E+00
10 6510 -9.879494E+03 1.000000E+00
11 7161 -9.860295E+03 1.000000E+00
12 7812 -9.833188E+03 1.000000E+00
13 8463 -9.816950E+03 1.000000E+00
14 9114 -9.782503E+03 1.000000E+00
15 9765 -9.749390E+03 1.000000E+00
16 10416 -9.693519E+03 1.000000E+00
17 11067 -9.599986E+03 1.000000E+00
18 11718 -9.168946E+03 1.000000E+00
19 12369 -9.092169E+03 1.000000E+00
20 13020 -9.093662E+03 1.000000E+00
21 13671 -9.093657E+03 1.000000E+00
22 14322 -9.094858E+03 1.000000E+00
23 14974 -9.095454E+03 1.000000E+00
24 15626 -9.095979E+03 1.000000E+00
25 16278 -9.096814E+03 1.000000E+00
26 16930 -9.098240E+03 1.000000E+00
27 17582 -9.100739E+03 1.000000E+00
28 18233 -9.106769E+03 1.000000E+00
29 18884 -9.110046E+03 1.000000E+00
30 19535 -9.114624E+03 1.000000E+00
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Content source: CISprague/Astro.IQ
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