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

Federici, Lorenzo; Benedikter, Boris; Zavoli, Alessandro

doi:10.1109/cec48606.2020.9185800

Cited by 19 publications

(8 citation statements)

References 34 publications

(39 reference statements)

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“…The solution of the resulting constrained global optimization problem is carried out by an in-house code named "Evolutionary Optimization at Sapienza" (EOS) [28], which implements a selfadaptive, ε-constrained, partially restarted differential evolution algorithm, with a synchronous island-model to handle multiple subpopulations as parallel processes, with sporadic migrations of individuals between them. EOS has been successfully applied to a broad range of unconstrained and constrained space trajectory optimization problems, as multiple gravity-assist trajectories [29,30], rocket ascent trajectories [31], and multirendezvous missions [32].…”

Section: B Global Optimization Algorithmmentioning

confidence: 99%

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

Federici

Zavoli

Colasurdo

et al. 2021

Journal of Spacecraft and Rockets

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Section: B Global Optimization Algorithmmentioning

confidence: 99%

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

Federici

Zavoli

Colasurdo

et al. 2021

Journal of Spacecraft and Rockets

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“…The inner-level problem is tackled in this paper by EOS (Evolutionary Optimization at Sapienza) [48]. 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.…”

Section: Inner-levelmentioning

confidence: 99%

“…Interested readers are suggested to refer to Ref. [48] for further details about the algorithm.…”

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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“…To validate the quality of attained results, the same problem was solved also using EOS, 45 a direct shooting algorithm based on differential evolution that was already successfully employed to solve a similar instance of the problem at hand. 46 The comparison between the two solutions is reported in Table 7.…”

Section: Unconstrained Returnmentioning

confidence: 99%

Convex Optimization of Launch Vehicle Ascent Trajectory with Heat-Flux and Splash-Down Constraints

Benedikter¹,

Zavoli²,

Colasurdo³

et al. 2020

Preprint

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This paper presents a convex programming approach to the optimization of a multistage launch vehicle ascent trajectory, from the liftoff to the payload injection into the target orbit, taking into account multiple nonconvex constraints, such as the maximum heat flux after fairing jettisoning and the splash-down of the burned-out stages. Lossless and successive convexification are employed to convert the problem into a sequence of convex subproblems. Virtual controls and buffer zones are included to ensure the recursive feasibility of the process and a state-of-the-art method for updating the reference solution is implemented to filter out undesired phenomena that may hinder convergence. A hp pseudospectral discretization scheme is used to accurately capture the complex ascent and return dynamics with a limited computational effort. The convergence properties, computational efficiency, and robustness of the algorithm are discussed on the basis of numerical results. The ascent of the VEGA launch vehicle toward a polar orbit is used as case study to discuss the interaction between the heat flux and splash-down constraints. Finally, a sensitivity analysis of the launch vehicle carrying capacity to different splash-down locations is presented.

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

Cited by 19 publications

References 34 publications

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

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

Evolutionary Optimization of Multirendezvous Impulsive Trajectories

Convex Optimization of Launch Vehicle Ascent Trajectory with Heat-Flux and Splash-Down Constraints

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