Global Optimization with Non-Convex Constraints

2000 ◽  
Author(s):  
Roman G. Strongin ◽  
Yaroslav D. Sergeyev
Keyword(s):  
Global Optimization ◽  
Convex Constraints

Low-thrust trajectory design as a constrained global optimization problem

2011 ◽  
Vol 225 (11) ◽  
pp. 1243-1251 ◽  
Author(s):  
C H Yam ◽  
D D Lorenzo ◽  
D Izzo
Keyword(s):  
Global Optimization ◽  
Electric Propulsion ◽  
Optimization Problem ◽  
Solution Space ◽  
Optimization Techniques ◽  
Trajectory Design ◽  
Global Optimization Problem ◽  
Constrained Global Optimization ◽  
Low Thrust ◽  
Convex Constraints

The design of a spacecraft trajectory can be formulated as a global optimization task. The complexity of the resulting problem depends greatly on the final target planet, the chosen spacecraft intermediate route, and the type of engine and power system available on-board. Few attempts have been made to directly use a global optimization framework to design trajectories that make use of low-thrust propulsion because of the large scale and extreme complexity of the resulting non-linear programming problem. The presence of non-convex constraints, in particular, requires the use of solvers able to deal with such an added complexity. Here, the Sims–Flanagan transcription method is proposed to model the low-thrust trajectory design as a constrained global optimization problem. Then, two different solvers are applied: basin hopping and simulated annealing with adaptive neighbourhood. Both algorithms are hybridized with a local search. Two different interplanetary trajectories are considered: an Earth–Earth–Jupiter transfer with a nuclear electric propulsion spacecraft inspired by the Jupiter Icy Moons Orbiter and a transfer to Mercury inspired by the BepiColombo mission. For both problems, the proposed approach proves to be able to explore automatically the vast solution space, producing a large number of trajectories in a large range of final mass and flight times, proving the possibility to apply global optimization techniques directly to the low-thrust problem.

Global optimization method with dual Lipschitz constant estimates for problems with non-convex constraints

2020 ◽  
Vol 24 (16) ◽  
pp. 11853-11865
Author(s):  
Roman Strongin ◽  
Konstantin Barkalov ◽  
Semen Bevzuk
Keyword(s):  
Global Optimization ◽  
Lipschitz Constant ◽  
Optimization Method ◽  
Global Optimization Method ◽  
Convex Constraints

Global Optimization

1993 ◽  
Author(s):  
Reiner Horst ◽  
Hoang Tuy
Keyword(s):  
Global Optimization

Probability Models in Global Optimization

Informatica ◽  
2016 ◽  
Vol 27 (2) ◽  
pp. 323-334 ◽  
Author(s):  
James M. Calvin
Keyword(s):  
Global Optimization ◽  
Probability Models
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