344 research outputs found
Multi-rendezvous Spacecraft Trajectory Optimization with Beam P-ACO
The design of spacecraft trajectories for missions visiting multiple
celestial bodies is here framed as a multi-objective bilevel optimization
problem. A comparative study is performed to assess the performance of
different Beam Search algorithms at tackling the combinatorial problem of
finding the ideal sequence of bodies. Special focus is placed on the
development of a new hybridization between Beam Search and the Population-based
Ant Colony Optimization algorithm. An experimental evaluation shows all
algorithms achieving exceptional performance on a hard benchmark problem. It is
found that a properly tuned deterministic Beam Search always outperforms the
remaining variants. Beam P-ACO, however, demonstrates lower parameter
sensitivity, while offering superior worst-case performance. Being an anytime
algorithm, it is then found to be the preferable choice for certain practical
applications.Comment: Code available at https://github.com/lfsimoes/beam_paco__gtoc
Evolutionary Optimization for Active Debris Removal Mission Planning
Active debris removal missions require an accurate planning for maximizing mission payout, by reaching the maximum number of potential orbiting targets in a given region of space. Such a problem is known to be computationally demanding and the present paper provides a technique for preliminary mission planning based on a novel evolutionary optimization algorithm, which identifies the best sequence of debris to be captured and/or deorbited. A permutation-based encoding is introduced, which may handle multiple spacecraft trajectories. An original archipelago structure is also adopted for improving algorithm capabilities to explore the search space. As a further contribution, several crossover and mutation operators and migration schemes are tested in order to identify the best set of algorithm parameters for the considered class of optimization problems. The algorithm is numerically tested for a fictitious cloud of debris in the neighborhood of Sun-synchronous orbits, including cases with multiple chasers
Solving multi-objective dynamic travelling salesman problems by relaxation
This paper describes a method to solve Multi-objective Dynamic Travelling Salesman Problems. The problems are formulated as multi-objective hybrid optimal control problems, where the choice of the target destination for each phase is an integer variable. The resulting problem has thus a combinatorial nature in addition to being a multi-objective optimal control problem. The overall solution approach is based on a combination of the Multi Agent Collaborative Search, a population based memetic multi-objective optimisation algorithm, and the Direct Finite Elements Transcription, a direct method for optimal control problems. A relaxation approach is employed to transform the mixed integer problem into a purely continuous problem, and a set of smooth constraints is added in order to ensure that the relaxed variables of the final solution assume an integer value. A special set of smooth constraints is introduced in order to treat the mutually exclusive choices of the targets for each phase. The method is tested on two problems: the first is a motorised Travelling salesman problem described in the literature, the second is a space application where a satellite has to de-orbit multiple debris. For the first problem, the approach is generating better solutions than those reported in the literature
Efficiency of tree-search like heuristics to solve complex mixed-integer programming problems applied to the design of optimal space trajectories
In the past, space trajectory optimization was limited to optimal design of transfers to single
destinations, where optimality refers to minimum propellant consumption or transfer time. New
technologies, and a more daring approach to space, are today making the space community consider
missions that target multiple destinations.
In the present paper, we focus on missions that aim to visit multiple asteroids within a single launch.
The trajectory design of these missions is complicated by the fact that the asteroid sequences are not
known a priori but are the objective of the optimization itself. Usually, these problems are formulated as
global optimization (GO) problems, under the formulation of mixed-integer non-linear programming
(MINLP), on which the decision variables assume both continuous and discrete values. However, beyond
the aim of finding the global optimum, mission designers are usually interested in providing a wide range
of mission design options reflecting the multi-modality of the problems at hand. In this sense, a Constraint
Satisfaction Problem (CSP) formulation is also relevant.
In this manuscript, we focus on these two needs (i.e. tackling both the GO and the CSP) for the asteroid
tour problem. First, a tree-search algorithm based upon the Bellman’s principle of optimality is described
using dynamic programming approach to address the feasibility of solving the GO problem. This results in
an efficient and scalable procedure to obtain global optimum solutions within large datasets of asteroids.
Secondly, tree-search strategies like Beam Search and Ant Colony Optimization with back-tracking are
tested over the CSP formulations. Results reveal that BS handles better the multi-modality of the search
space when compared to ACO, as this latter solver has a bias towards elite solutions, which eventually
hinders the diversity needed to efficiently cope with CSP over graphs
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