Globally convergent evolution strategies for constrained optimization

Diouane, Youssef; Gratton, Serge; Vicente, Luís Nunes

doi:10.1007/s10589-015-9747-3

Cited by 18 publications

(24 citation statements)

References 47 publications

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“…However, ES have proven successful in solving non-convex optimization problems, e.g. [35], as well as in the global optimization of constraint problems [36]. This motivates the use of ES in the optimization problem presented here.…”

Section: B Evolution Strategiesmentioning

confidence: 97%

Application of Evolution Strategies to the Design of SAR Efficient Parallel Transmit Multi-Spoke Pulses for Ultra-High Field MRI

Eberhardt

Poser

Shah

et al. 2020

IEEE Trans. Med. Imaging

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Section: B Evolution Strategiesmentioning

confidence: 97%

Application of Evolution Strategies to the Design of SAR Efficient Parallel Transmit Multi-Spoke Pulses for Ultra-High Field MRI

Eberhardt

Poser

Shah

et al. 2020

IEEE Trans. Med. Imaging

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“…f (x (l) ) is set to the value of the objective function at the iteration l if x (l) satisfies the constraints, and set to +∞ otherwise. We note that we had to adapt the data profiles [40] to our constrained setting; for that reason, we are using the same convergence test as proposed in [41].…”

Section: Data Profilesmentioning

confidence: 99%

Upper trust bound feasibility criterion for mixed constrained Bayesian optimization with application to aircraft design

Priem

Bartoli

Diouane

et al. 2020

Aerospace Science and Technology

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Bayesian optimization methods have been successfully applied to black box optimization problems that are expensive to evaluate. In this paper, we adapt the so-called super efficient global optimization algorithm to solve more accurately mixed constrained problems. The proposed approach handles constraints by means of upper trust bound, the latter encourages exploration of the feasible domain by combining the mean prediction and the associated uncertainty function given by the Gaussian processes. On top of that, a refinement procedure, based on a learning rate criterion, is introduced to enhance the exploitation and exploration trade-off. We show the good potential of the approach on a set of numerical experiments. Finally, we present an application to conceptual aircraft configuration upon which we show the superiority of the proposed approach compared to a set of the state-of-the-art black box optimization solvers.

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“…A maximum value on the velocity model of m max = 4500m/s has been also imposed to avoid propagation by meaningless velocity models. Both requirements were guaranteed by projecting the offspring models onto the feasible domain defined by the bounds, an approach that has been shown to be globally convergent in [17]. Since there are no other constraints rather than simple bounds on the variables one can use the simple orthogonal 2 -projection.…”

Section: A Modified Cma-esmentioning

confidence: 99%

A parallel evolution strategy for an earth imaging problem in geophysics

et al. 2015

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In this paper we propose a new way to compute a rough approximation solution, to be later used as a warm starting point in a more refined optimization process, for a challenging global optimization problem related to Earth imaging in geophysics. The warm start consists of a velocity model that approximately solves a full-waveform inverse problem at low frequency. Our motivation arises from the availability of massively parallel computing platforms and the natural parallelization of evolution strategies as global optimization methods for continuous variables.Our first contribution consists of developing a new and efficient parametrization of the velocity models to significantly reduce the dimension of the original optimization space. Our second contribution is to adapt a class of evolution strategies to the specificity of the physical problem at hands where the objective function evaluation is known to be the most expensive computational part. A third contribution is the development of a parallel evolution strategy solver, taking advantage of a recently proposed modification of these class of evolutionary methods that ensures convergence and promotes better performance under moderate budgets.The numerical results presented demonstrate the effectiveness of the algorithm on a realistic 3D full-waveform inverse problem in geophysics. The developed numerical approach allows us to successfully solve an acoustic full-waveform inversion problem at low frequencies on a reasonable number of cores of a distributed memory computer.

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Globally convergent evolution strategies for constrained optimization

Cited by 18 publications

References 47 publications

Application of Evolution Strategies to the Design of SAR Efficient Parallel Transmit Multi-Spoke Pulses for Ultra-High Field MRI

Application of Evolution Strategies to the Design of SAR Efficient Parallel Transmit Multi-Spoke Pulses for Ultra-High Field MRI

Upper trust bound feasibility criterion for mixed constrained Bayesian optimization with application to aircraft design

A parallel evolution strategy for an earth imaging problem in geophysics

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