An efficient optimization method called 'Teaching-Learning-Based optimization (TLBO)' is proposed in this paper for large scale non-linear optimization problems for finding the global solutions. The proposed m...
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An efficient optimization method called 'Teaching-Learning-Based optimization (TLBO)' is proposed in this paper for large scale non-linear optimization problems for finding the global solutions. The proposed method is based on the effect of the influence of a teacher on the output of learners in a class. The basic philosophy of the method is explained in detail. The effectiveness of the method is tested on many benchmark problems with different characteristics and the results are compared with other population based methods. (C) 2011 Elsevier Inc. All rights reserved.
Inspired by the mechanism of the biological DNA, a DNA based genetic algorithm (DNA-GA) is proposed to determine the kinetic parameters for the hydrogenation reaction. The considered chemical process contains five rea...
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Inspired by the mechanism of the biological DNA, a DNA based genetic algorithm (DNA-GA) is proposed to determine the kinetic parameters for the hydrogenation reaction. The considered chemical process contains five reactions and 25 unknown parameters. The DNA-GA uses the DNA encoding method to represent the potential parameters and genetic operators inspired from the biological DNA are designed to find the global optimum. The study on the performance for typical benchmark functions shows that the DNA-GA outperforms the other two methods in both convergence speed and accuracy. Based on the operating data gathered from an industrial hydrogenation unit, 25 parameters are obtained by the DNA-GA and the kinetic model for the hydrogenation reaction is established. To verify the validity of the established model, another four groups of data are used to test the established model and two previously reported models. The comparison results show that the sum of square relative errors of the model obtained by the DNA-GA is the least of the test models, and its prediction is in good agreement with the practical operating data. (C) 2009 Elsevier B.V. All rights reserved.
One of the main challenges for ground-based optical astronomy is to compensate for atmospheric turbulence in near real-time. The goal is to obtain images as close as possible to the diffraction limit of the telescope....
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ISBN:
(纸本)9783030856656;9783030856649
One of the main challenges for ground-based optical astronomy is to compensate for atmospheric turbulence in near real-time. The goal is to obtain images as close as possible to the diffraction limit of the telescope. This challenge is addressed on the latest generation of giant optical telescopes by deploying multi-conjugate adaptive optics (MCAO) systems performing predictive tomography of the turbulence and multi-layer compensation. Such complex systems require a high fidelity estimate of the turbulence profile above the telescope, to be updated regularly during operations as turbulence conditions evolve. In this paper, we modify the traditional Levenberg-Marquardt (LM) algorithm by considering stochastically chosen subsystems of the full problem to identify the required parameters efficiently, while coping with the real-time challenge. While LM operates on the full set data samples, the resulting Stochastic LM (SLM) method randomly selects subsamples to compute corresponding approximate gradients and Hessians. Hence, SLM reduces the algorithmic complexity per iteration and shortens the overall time to solution, while maintaining LM's numerical robustness. We present a new convergence analysis for SLM, implement the algorithm with optimized GPU kernels, and deploy it on shared-memory systems with multiple GPU accelerators. We assess SLM in the adaptive optics system configurations in the context of the MCAO-Assisted Visible Imager & Spectrograph (MAVIS) instrument for the Very Large Telescope (VLT). We demonstrate performance superiority of SLM over the traditional LM algorithm and the classical stochastic first-order methods. At the scale of VLT AO, SLM finishes the optimization process and accurately retrieves the parameters (e.g., turbulence strength and wind speed profiles) in less than a second using up to eight NVIDIA A100 GPUs, which permits high acuity real-time throughput over a night of observations.
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