因为网上的pykep算法过时了，所以上传了两个使用pykep的算例，一个是mga_1dsm，一个是mga_lt_nep，适用于python==3.7.12，pykep==2.6，pygmo==2.18.0版本。
但是对于具体的算法，比如nlopt中的cobyla算法是什么，并没有多少了解，只是这样写能够work，只是这个算法可以处理nonlinear constrain，而ipopt不行（不会设置gradient），nega2不行（不能解这种类型的问题），供大家参考。
pykep是用来设置问题的参数，pygmo是用来设置求解的算法。
为了避免冲突，需要先安装pygmo，再安装pykep.
1.eveej_pso.py是用粒子群算法优化的，求出来的结果（只代表本次优化过程）是：
Running a 粒子群优化 Done!! Solutions found are: [13477.57246471] First Leg: earth to venus Departure: 2026-May-31 10:55:44.408813 (9647.455375102 mjd2000) Duration: 183.2475506065751days VINF: 0.5 km/sec DSM after 63.28593125466642 days DSM magnitude: 2573.7350174836038m/s
leg no. 2: venus to earth Duration: 331.8344858452072days Fly-by epoch: 2026-Nov-30 16:52:12.781221 (9830.702925708574 mjd2000) Fly-by radius: 19.638534868101782 planetary radii DSM after 58.86912433451865 days DSM magnitude: 1648.0053370206726m/s
leg no. 3: earth to earth Duration: 371.2095624791099days Fly-by epoch: 2027-Oct-28 12:53:52.358247 (10162.537411553782 mjd2000) Fly-by radius: 19.254657222855847 planetary radii DSM after 81.44487852168257 days DSM magnitude: 353.45540091583905m/s
leg no. 4: earth to jupiter Duration: 1063.3929599076166days Fly-by epoch: 2028-Nov-02 17:55:38.556442 (10533.746974032892 mjd2000) Fly-by radius: 1.1000091823515603 planetary radii DSM after 106.33929599076167 days DSM magnitude: 2939.0487421303183m/s
Arrival at jupiter Arrival epoch: 2031-Oct-02 03:21:30.292460 (11597.139933940509 mjd2000) Arrival Vinf: 5463.327967163263m/s Total mission time: 5.337945404075315 years (1949.6845588385088 days)
2.eveej_sade.py是用自适应差分进化算法优化的，求出来的结果（只代表本次优化过程）是：
Running a Self-Adaptive Differential Evolution Algorithm .... on 10 parallel islands Done!! Solutions found are: [array([15713.05664449]), array([16656.70413232]), array([14279.91109301]), array([15135.13956289]), array([16224.04756102]), array([14285.14082974]), array([14669.08043566]), array([17742.36866846]), array([17591.52337764]), array([15219.12117911])] First Leg: earth to venus Departure: 2029-Jul-13 07:13:08.776618 (10786.300796025673 mjd2000) Duration: 251.63805121091218days VINF: 0.5986942562577509 km/sec DSM after 72.30612279191512 days DSM magnitude: 3111.501468631372m/s
leg no. 2: venus to earth Duration: 298.8801281125808days Fly-by epoch: 2030-Mar-21 22:31:56.401241 (11037.938847236584 mjd2000) Fly-by radius: 6.078092088349982 planetary radii DSM after 82.05682721771234 days DSM magnitude: 455.0872958396139m/s
leg no. 3: earth to earth Duration: 363.1291614647999days Fly-by epoch: 2031-Jan-14 19:39:19.470168 (11336.818975349166 mjd2000) Fly-by radius: 24.223585805875857 planetary radii DSM after 167.3553557442532 days DSM magnitude: 160.29373633443353m/s
leg no. 4: earth to jupiter Duration: 1095.682259836169days Fly-by epoch: 2032-Jan-12 22:45:19.020727 (11699.948136813966 mjd2000) Fly-by radius: 1.1375040798527287 planetary radii DSM after 209.5309864766393 days DSM magnitude: 5612.921315074164m/s
Arrival at jupiter Arrival epoch: 2035-Jan-12 15:07:46.270572 (12795.630396650135 mjd2000) Arrival Vinf: 4341.413020874829m/s Total mission time: 5.501244628677513 years (2009.3296006244618 days)
3.从结果分析，DSM都很大，现实世界不会使用这么大的DSM，但是尚未找到限制DSM的参数。
backcall 0.2.0 pyh9f0ad1d_0 conda-forge
backports 1.0 pyhd8ed1ab_3 conda-forge
backports.functools_lru_cache 2.0.0 pyhd8ed1ab_0 conda-forge
boost 1.74.0 py37h457096e_5 conda-forge
boost-cpp 1.74.0 h9f4b32c_8 conda-forge
bzip2 1.0.8 hcfcfb64_5 conda-forge
ca-certificates 2024.2.2 h56e8100_0 conda-forge
certifi 2024.2.2 pyhd8ed1ab_0 conda-forge
cloudpickle 2.2.1 pyhd8ed1ab_0 conda-forge
colorama 0.4.6 pyhd8ed1ab_0 conda-forge
cycler 0.11.0 pyhd8ed1ab_0 conda-forge
debugpy 1.6.3 py37hf2a7229_0 conda-forge
decorator 4.4.2 py_0 conda-forge
entrypoints 0.4 pyhd8ed1ab_0 conda-forge
freetype 2.12.1 hdaf720e_2 conda-forge
importlib-metadata 6.7.0 pypi_0 pypi
intel-openmp 2024.0.0 h57928b3_49841 conda-forge
ipopt 3.14.9 h0ff4939_1 conda-forge
ipykernel 6.16.2 pyh025b116_0 conda-forge
ipyparallel 8.6.1 pyhd8ed1ab_0 conda-forge
ipython 7.33.0 py37h03978a9_0 conda-forge
jedi 0.19.1 pyhd8ed1ab_0 conda-forge
joblib 1.3.2 pypi_0 pypi
jpeg 9e hcfcfb64_3 conda-forge
jupyter_client 7.4.9 pyhd8ed1ab_0 conda-forge
jupyter_core 4.11.1 py37h03978a9_0 conda-forge
kiwisolver 1.3.2 py37h8c56517_1 conda-forge
lcms2 2.14 h90d422f_0 conda-forge
lerc 4.0.0 h63175ca_0 conda-forge
libblas 3.9.0 21_win64_mkl conda-forge
libcblas 3.9.0 21_win64_mkl conda-forge
libdeflate 1.14 hcfcfb64_0 conda-forge
libflang 5.0.0 h6538335_20180525 conda-forge
libhwloc 2.9.3 default_haede6df_1009 conda-forge
libiconv 1.17 hcfcfb64_2 conda-forge
liblapack 3.9.0 21_win64_mkl conda-forge
libpng 1.6.43 h19919ed_0 conda-forge
libsodium 1.0.18 h8d14728_1 conda-forge
libsqlite 3.45.2 hcfcfb64_0 conda-forge
libtiff 4.4.0 hc4f729c_5 conda-forge
libwebp-base 1.3.2 hcfcfb64_0 conda-forge
libxcb 1.13 hcd874cb_1004 conda-forge
libxml2 2.12.6 hc3477c8_1 conda-forge
libzlib 1.2.13 hcfcfb64_5 conda-forge
llvm-meta 5.0.0 0 conda-forge
llvmlite 0.39.1 pypi_0 pypi
loguru 0.7.2 pypi_0 pypi
m2w64-gcc-libgfortran 5.3.0 6 conda-forge
m2w64-gcc-libs 5.3.0 7 conda-forge
m2w64-gcc-libs-core 5.3.0 7 conda-forge
m2w64-gmp 6.1.0 2 conda-forge
m2w64-libwinpthread-git 5.0.0.4634.697f757 2 conda-forge
matplotlib-base 3.4.3 py37h4a79c79_2 conda-forge
matplotlib-inline 0.1.6 pyhd8ed1ab_0 conda-forge
metis 5.1.0 h63175ca_1007 conda-forge
mkl 2024.0.0 h66d3029_49657 conda-forge
msys2-conda-epoch 20160418 1 conda-forge
mumps-seq 5.2.1 h1f49738_14 conda-forge
nest-asyncio 1.6.0 pyhd8ed1ab_0 conda-forge
networkx 2.5.1 pyhd8ed1ab_0 conda-forge
nlopt 2.7.1 py37h1d391ab_1 conda-forge
numba 0.56.4 pypi_0 pypi
numpy 1.21.6 py37h2830a78_0 conda-forge
openjpeg 2.5.0 hc9384bd_1 conda-forge
openmp 5.0.0 vc14_1 conda-forge
openssl 3.2.1 hcfcfb64_1 conda-forge
packaging 23.2 pyhd8ed1ab_0 conda-forge
pagmo 2.18.0 h1c250d2_4 conda-forge
parso 0.8.3 pyhd8ed1ab_0 conda-forge
pickleshare 0.7.5 py_1003 conda-forge
pillow 9.2.0 py37h42a8222_2 conda-forge
pip 24.0 pyhd8ed1ab_0 conda-forge
prompt-toolkit 3.0.42 pyha770c72_0 conda-forge
psutil 5.9.3 py37h51bd9d9_0 conda-forge
pthread-stubs 0.4 hcd874cb_1001 conda-forge
pthreads-win32 2.9.1 hfa6e2cd_3 conda-forge
pybind11-abi 4 hd8ed1ab_3 conda-forge
pygments 2.17.2 pyhd8ed1ab_0 conda-forge
pygmo 2.18.0 py37h8d76dce_2 conda-forge
pykep 2.6 py37h959d761_5 conda-forge
pyparsing 3.1.2 pyhd8ed1ab_0 conda-forge
python 3.7.12 h900ac77_100_cpython conda-forge
python-dateutil 2.9.0 pyhd8ed1ab_0 conda-forge
python_abi 3.7 4_cp37m conda-forge
pywin32 303 py37hcc03f2d_0 conda-forge
pyzmq 24.0.1 py37h7347f05_0 conda-forge
scikit-learn 1.0.2 pypi_0 pypi
scipy 1.7.3 py37hb6553fb_0 conda-forge
setuptools 65.6.3 pyhd8ed1ab_0 conda-forge
six 1.16.0 pyh6c4a22f_0 conda-forge
sqlite 3.45.2 hcfcfb64_0 conda-forge
tbb 2021.11.0 h91493d7_1 conda-forge
threadpoolctl 3.1.0 pypi_0 pypi
tk 8.6.13 h5226925_1 conda-forge
tornado 6.2 py37hcc03f2d_0 conda-forge
tqdm 4.66.2 pyhd8ed1ab_0 conda-forge
traitlets 5.9.0 pyhd8ed1ab_0 conda-forge
typing-extensions 4.7.1 pypi_0 pypi
ucrt 10.0.22621.0 h57928b3_0 conda-forge
vc 14.3 hcf57466_18 conda-forge
vc14_runtime 14.38.33130 h82b7239_18 conda-forge
vs2015_runtime 14.38.33130 hcb4865c_18 conda-forge
wcwidth 0.2.10 pyhd8ed1ab_0 conda-forge
wheel 0.34.2 py37_0 conda-forge
win32-setctime 1.1.0 pypi_0 pypi
xorg-libxau 1.0.11 hcd874cb_0 conda-forge
xorg-libxdmcp 1.1.3 hcd874cb_0 conda-forge
xz 5.2.6 h8d14728_0 conda-forge
zeromq 4.3.4 h0e60522_1 conda-forge
zipp 3.15.0 pypi_0 pypi
zstd 1.5.5 h12be248_0 conda-forge