Improved stochastic optimization algorithms for adaptive optics

Kalogeropoulos, T.E.; Saridakis, Y. G.; Zakynthinaki, Maria S.

doi:10.1016/s0010-4655(96)00101-4

Cited by 13 publications

(24 citation statements)

References 14 publications

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“…In order to improve the exploration phase, we use Cauchy distribution for the adjustment of the variables and Gaussian distribution in the phase of the exploitation. In an effort to speed up the exploration/exploitation of the area of the current optimum, we create the elements of the new harmony by modifying variables in a manner similar to that in algorithm ALOPEX IV [9], making the adjustment process slightly deterministic. Last but not least, in order to avoid computations that either won't improve the optimum or the harmony memory isn't updated with new better harmonies, in addition to the maximum number of iteration we add another termination criterion:…”

Section: B Problemmentioning

confidence: 99%

A new approach in image registration

Spanakis

Mathioudakis

Kampanis

et al. 2016

2016 IEEE International Conference on Imaging Systems and Techniques (IST)

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Evolutionary algorithms are metaheuristic algorithms that mimic biological mechanisms for the solution of optimization problems. They are widely used in many problems of engineering, including image analysis and pattern recognition. Such methods had shown added value in overcoming certain problems and limitiations of more traditional methods and approaches. This is particularlay interesting in Computer for designing faster and more accurate optimization methods. Despite recent advances in image registration, there is still room for improvement especially with the use of metaheuristic algorithms. In this paper, we present a novel method for solving the automated rigid registration problem by introducing a variant of the relatively new metaheuristic method called harmony search which shows promising results in difficult rigid registration cases.

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Section: B Problemmentioning

confidence: 99%

A new approach in image registration

Spanakis

Mathioudakis

Kampanis

et al. 2016

2016 IEEE International Conference on Imaging Systems and Techniques (IST)

View full text Add to dashboard Cite

show abstract

“…It was originally devised in [8,9] for the purpose of experimentally determining receptive fields of individual neurons in the visual pathway. We modified the ALOPEX optimization in [5,10,11] and introduced new versions in addition to the ones already known.…”

Section: Alopex Stochastic Optimizationmentioning

confidence: 99%

“…The noise terms g i are essential ingredients in the process, as they provide the agitation necessary to drive the process [1,5,[8][9][10][11]. The dynamics of the process depends strongly on the mean amplitude of the g i terms.…”

Section: ) I and A (2) Imentioning

confidence: 99%

“…For the optimization of the image, ALOPEX stochastic optimization is used (see Section 3 and [1,5,[8][9][10][11]). ALOPEX optimization methods are driven by the parallel incoherent dithering of the control variables and the time dependence of the feedback.…”

Section: Introductionmentioning

confidence: 99%

“…The method's effectiveness and practicality depend on the ability to follow the motion of the turbulent wavefronts. ALOPEX stochastic optimization algorithm is chosen due to its speed of convergence and its simple implementation [1,5,10,11].…”

Section: Introductionmentioning

confidence: 99%

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Stochastic optimization for a tip-tilt adaptive correcting system

Zakynthinaki

Saridakis

2003

Computer Physics Communications

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We present computer simulations of a tip-tilt adaptive optics system, where stochastic optimization is applied to the problem of dynamic compensation of atmospheric turbulence. The system uses a simple measure of the light intensity that passes through a mask and is recorded on the image plane, to generate signals for the tip-tilt mirror. A feedback system rotates the mirror adaptively and in phase with the rapidly changing atmospheric conditions. Computer simulations and a series of numerical experiments investigate the implementation of the method in the presence of drifting atmosphere. In particular, the study examines the system's sensitivity to the rate of change of the atmospheric conditions and investigates the optimal size of the mirror's masking area and the algorithm's optimal degree of stochasticity.

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Stochastic optimization for modeling physiological time series: application to the heart rate response to exercise

Zakynthinaki

Stirling

2007

Computer Physics Communications

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Improved stochastic optimization algorithms for adaptive optics

Cited by 13 publications

References 14 publications

A new approach in image registration

A new approach in image registration

Stochastic optimization for a tip-tilt adaptive correcting system

Stochastic optimization for modeling physiological time series: application to the heart rate response to exercise

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