Integrated Optimization of First-Stage SRM and Ascent Trajectory of Multistage Launch Vehicles

Federici, Lorenzo; Zavoli, Alessandro; Colasurdo, Guido; Mancini, L.; Neri, Agostino

doi:10.2514/1.a34930

Cited by 16 publications

(8 citation statements)

References 26 publications

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“…EOS is an inhouse optimization code for continuous-variable problems, which implements an improved self-adaptive, partially restarted Differential Evolution (DE) algorithm, with a synchronous island model to handle multiple populations in parallel. EOS was developed in the contest of the Global Trajectory Optimization Competitions [49,50] and has proven to be a flexible and high-performance algorithm, able to successfully cope with a broad range of real-world unconstrained and constrained space trajectory optimization problems [51,52]. The values of the hyperparameters used in this work are reported in Table 1.…”

Section: Inner-levelmentioning

confidence: 99%

Evolutionary Optimization of Multirendezvous Impulsive Trajectories

Federici

Zavoli

Colasurdo

2021

International Journal of Aerospace Engineering

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This paper investigates the use of evolutionary algorithms for the optimization of time-constrained impulsive multirendezvous missions. The aim is to find the minimum- Δ V trajectory that allows a chaser spacecraft to perform, in a prescribed mission time, a complete tour of a set of targets, such as space debris or artificial satellites, which move on the same orbital plane at slightly different altitudes. For this purpose, a two-level design approach is pursued. First, an outer-level combinatorial problem is defined, dealing with the simultaneous optimization of the sequence of targets and the rendezvous epochs. The suggested approach is first tested by assuming that all transfer legs last exactly the same amount of time; then, the time domain is discretized over a finer grid, allowing a more appropriate sizing of the time window allocated for each leg. The outer-level problem is solved by an in-house genetic algorithm, which features an effective permutation-preserving solution encoding. A simple, but fairly accurate, heuristic, based on a suboptimal four-impulse analytic solution of the single-target rendezvous problem, is used when solving the combinatorial problem for a fast guess at the transfer cost, given the departure and arrival epochs. The outer-level problem solution is used to define an inner-level NLP problem, concerning the optimization of each body-to-body transfer leg. In this phase, the encounter times are further refined. The inner-level problem is tackled through an in-house multipopulation self-adaptive differential evolution algorithm. Numerical results for case studies including up to 20 targets with different time grids are presented.

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Section: Inner-levelmentioning

confidence: 99%

Evolutionary Optimization of Multirendezvous Impulsive Trajectories

Federici

Zavoli

Colasurdo

2021

International Journal of Aerospace Engineering

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“…This section presents the main results achieved by EOS when applied to challenging, real-world space trajectory optimization problems. More detailed information about how the three optimization problems that follow have been formulated (that is, their objective function, design variables, bounding intervals and constraints) are reported in the corresponding references [2], [3], [6].…”

Section: Application To Real-world Space Trajectory Optimization Prob...mentioning

confidence: 99%

“…Multiple design constraints involving the solid rocket motor or dependent on the actual flight trajectory were also enforced. Here the problem was even more complex, because of the large number of constraints and its multi-disciplinarity (the design variables represent very distant physical quantities); nevertheless, EOS was still able to attain competitive results [3].…”

Section: B Ascent Trajectory Optimization Of Vega Launch Vehiclesmentioning

confidence: 99%

EOS: a Parallel, Self-Adaptive, Multi-Population Evolutionary Algorithm for Constrained Global Optimization

Federici

Benedikter

Zavoli

2020

2020 IEEE Congress on Evolutionary Computation (CEC)

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This paper presents the main characteristics of the evolutionary optimization code named EOS, Evolutionary Optimization at Sapienza, and its successful application to challenging, real-world space trajectory optimization problems. EOS is a global optimization algorithm for constrained and unconstrained problems of real-valued variables. It implements a number of improvements to the well-known Differential Evolution (DE) algorithm, namely, a self-adaptation of the control parameters, an epidemic mechanism, a clustering technique, an ε-constrained method to deal with nonlinear constraints, and a synchronous island-model to handle multiple populations in parallel. The results reported prove that EOS is capable of achieving increased performance compared to state-of-the-art single-population selfadaptive DE algorithms when applied to high-dimensional or highly-constrained space trajectory optimization problems.

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“…In the present paper, the inner level optimization is carried out by using an in-house optimization code named EOS (Evolutionary Optimization at Sapienza), developed in the contest of the Global Trajectory Optimization Competitions 25,26 and previously applied with success to both unconstrained 27 and constrained 28 space trajectory optimization problems. EOS implements a multi-population self-adaptive Differential Evolution (DE) algorithm, with a synchronous island-model for parallel computation.…”

Section: Differential Evolutionmentioning

confidence: 99%

A Time-Dependent TSP Formulation for the Design of an Active Debris Removal Mission using Simulated Annealing

Federici¹,

Zavoli²,

Colasurdo³

2019

Preprint

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This paper proposes a formulation of the Active Debris Removal (ADR) Mission Design problem as a modified Time-Dependent Traveling Salesman Problem (TDTSP). The TDTSP is a well-known combinatorial optimization problem, whose solution is the cheapest monocyclic tour connecting a number of non-stationary cities in a map. The problem is tackled with an optimization procedure based on Simulated Annealing, that efficiently exploits a natural encoding and a careful choice of mutation operators. The developed algorithm is used to simultaneously optimize the targets sequence and the rendezvous epochs of an impulsive ADR mission. Numerical results are presented for sets comprising up to 20 targets.

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Integrated Optimization of First-Stage SRM and Ascent Trajectory of Multistage Launch Vehicles

Cited by 16 publications

References 26 publications

Evolutionary Optimization of Multirendezvous Impulsive Trajectories

Evolutionary Optimization of Multirendezvous Impulsive Trajectories

EOS: a Parallel, Self-Adaptive, Multi-Population Evolutionary Algorithm for Constrained Global Optimization

A Time-Dependent TSP Formulation for the Design of an Active Debris Removal Mission using Simulated Annealing

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