83,453 research outputs found

    Optimization for source localization and geoacoustic inversion in underwater acoustics

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    Matched-field inversion techniques are widely used for source localization and geoacoustic parameter estimation. These inversion methods correlate the received data with modeled data and find the model parameters which provide the maximum correlation. However, when a large number of unknown parameters is involved, many modeled data need to be generated and correlated with the observed data and thus, matched-field inversion can be computationally intensive. An optimization process applied to matched-field inversion is often required to accelerate the inversion process. In this work, tabu is applied to matched-field inversion for source localization and environmental parameter estimation. Tabu is a global optimization technique which proceeds by finding the best model in a local neighborhood, where a best model is defined as the set of parameter values that provides the maximum correlation in a given neighborhood. However, the search moves beyond local areas by maintaining records of past moves. Using historical information, the approach avoids certain paths. Thus, tabu limits the search space and redefines neighborhoods in each iteration. Tabu is evaluated through a comparison to fast simulated annealing. To improve efficiency, a tabu approach is also developed for parameter estimation in a rotated coordinate system. Rotation is achieved through the identification of combinations of parameters that affect acoustic field computations

    State Transition Algorithm

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    In terms of the concepts of state and state transition, a new heuristic random search algorithm named state transition algorithm is proposed. For continuous function optimization problems, four special transformation operators called rotation, translation, expansion and axesion are designed. Adjusting measures of the transformations are mainly studied to keep the balance of exploration and exploitation. Convergence analysis is also discussed about the algorithm based on random search theory. In the meanwhile, to strengthen the search ability in high dimensional space, communication strategy is introduced into the basic algorithm and intermittent exchange is presented to prevent premature convergence. Finally, experiments are carried out for the algorithms. With 10 common benchmark unconstrained continuous functions used to test the performance, the results show that state transition algorithms are promising algorithms due to their good global search capability and convergence property when compared with some popular algorithms.Comment: 18 pages, 28 figure

    Classical Optimizers for Noisy Intermediate-Scale Quantum Devices

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    We present a collection of optimizers tuned for usage on Noisy Intermediate-Scale Quantum (NISQ) devices. Optimizers have a range of applications in quantum computing, including the Variational Quantum Eigensolver (VQE) and Quantum Approximate Optimization (QAOA) algorithms. They are also used for calibration tasks, hyperparameter tuning, in machine learning, etc. We analyze the efficiency and effectiveness of different optimizers in a VQE case study. VQE is a hybrid algorithm, with a classical minimizer step driving the next evaluation on the quantum processor. While most results to date concentrated on tuning the quantum VQE circuit, we show that, in the presence of quantum noise, the classical minimizer step needs to be carefully chosen to obtain correct results. We explore state-of-the-art gradient-free optimizers capable of handling noisy, black-box, cost functions and stress-test them using a quantum circuit simulation environment with noise injection capabilities on individual gates. Our results indicate that specifically tuned optimizers are crucial to obtaining valid science results on NISQ hardware, and will likely remain necessary even for future fault tolerant circuits

    Evolutionary neurocontrol: A novel method for low-thrust gravity-assist trajectory optimization

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    This article discusses evolutionary neurocontrol, a novel method for low-thrust gravity-assist trajectory optimization
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