14,400 research outputs found
A hybrid swarm-based algorithm for single-objective optimization problems involving high-cost analyses
In many technical fields, single-objective optimization procedures in
continuous domains involve expensive numerical simulations. In this context, an
improvement of the Artificial Bee Colony (ABC) algorithm, called the Artificial
super-Bee enhanced Colony (AsBeC), is presented. AsBeC is designed to provide
fast convergence speed, high solution accuracy and robust performance over a
wide range of problems. It implements enhancements of the ABC structure and
hybridizations with interpolation strategies. The latter are inspired by the
quadratic trust region approach for local investigation and by an efficient
global optimizer for separable problems. Each modification and their combined
effects are studied with appropriate metrics on a numerical benchmark, which is
also used for comparing AsBeC with some effective ABC variants and other
derivative-free algorithms. In addition, the presented algorithm is validated
on two recent benchmarks adopted for competitions in international conferences.
Results show remarkable competitiveness and robustness for AsBeC.Comment: 19 pages, 4 figures, Springer Swarm Intelligenc
Metaheuristic Algorithms for Convolution Neural Network
A typical modern optimization technique is usually either heuristic or
metaheuristic. This technique has managed to solve some optimization problems
in the research area of science, engineering, and industry. However,
implementation strategy of metaheuristic for accuracy improvement on
convolution neural networks (CNN), a famous deep learning method, is still
rarely investigated. Deep learning relates to a type of machine learning
technique, where its aim is to move closer to the goal of artificial
intelligence of creating a machine that could successfully perform any
intellectual tasks that can be carried out by a human. In this paper, we
propose the implementation strategy of three popular metaheuristic approaches,
that is, simulated annealing, differential evolution, and harmony search, to
optimize CNN. The performances of these metaheuristic methods in optimizing CNN
on classifying MNIST and CIFAR dataset were evaluated and compared.
Furthermore, the proposed methods are also compared with the original CNN.
Although the proposed methods show an increase in the computation time, their
accuracy has also been improved (up to 7.14 percent).Comment: Article ID 1537325, 13 pages. Received 29 January 2016; Revised 15
April 2016; Accepted 10 May 2016. Academic Editor: Martin Hagan. in Hindawi
Publishing. Computational Intelligence and Neuroscience Volume 2016 (2016
Optimized pulses for the control of uncertain qubits
Constructing high-fidelity control fields that are robust to control, system,
and/or surrounding environment uncertainties is a crucial objective for quantum
information processing. Using the two-state Landau-Zener model for illustrative
simulations of a controlled qubit, we generate optimal controls for \pi/2- and
\pi-pulses, and investigate their inherent robustness to uncertainty in the
magnitude of the drift Hamiltonian. Next, we construct a quantum-control
protocol to improve system-drift robustness by combining environment-decoupling
pulse criteria and optimal control theory for unitary operations. By
perturbatively expanding the unitary time-evolution operator for an open
quantum system, previous analysis of environment-decoupling control pulses has
calculated explicit control-field criteria to suppress environment-induced
errors up to (but not including) third order from \pi/2- and \pi-pulses. We
systematically integrate this criteria with optimal control theory,
incorporating an estimate of the uncertain parameter, to produce improvements
in gate fidelity and robustness, demonstrated via a numerical example based on
double quantum dot qubits. For the qubit model used in this work, post facto
analysis of the resulting controls suggests that realistic control-field
fluctuations and noise may contribute just as significantly to gate errors as
system and environment fluctuations.Comment: 38 pages, 15 figures, RevTeX 4.1, minor modifications to the previous
versio
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