This paper compares optimization algorithms used to automate parameter extraction for SiC power MOSFET models. New methods for evaluating and ranking optimization algorithms are developed, which are relevant for engin...
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The convergence of machine learning and edge computing has led to the development of scalable solutions that bring computation closer to the data source. However, optimizing machine learning models efficiently for edg...
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In recent years, multimodal multiobjective optimization algorithms (MMOAs) based on evolutionary computation have been widely studied. However, existing MMOAs are mainly tested on benchmark function sets such as the 2...
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In the backdrop of the increasingly complex power systems, traditional grid dispatching methods struggle to cope with diverse challenges. Therefore, there is a need to introduce artificial intelligence techniques to e...
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In decomposition-based evolutionary multi-objective optimization algorithms (MOEA/Ds) with adaptive strategies for weight vectors, the vectors are updated periodically. Their updates' timing and frequency signific...
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This paper introduces a novel concept for customer load scheduling in the Smart Grid (SG). The concept is based on the forthcoming internet of things (IoT). Approximate optimization algorithms are deduced for optimum ...
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We evaluate five optimization algorithms for laser pulse temporal shape optimization, using a semi-physical model of a high-power laser. Hybrid algorithms combine Differential Evolution and Bayesian optimization algor...
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In the context of the current transition in energy structures and the development of renewable energy, optimizing the layout of solar energy systems along highways has become a significant research topic. This study e...
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The performance of population-based multiobjective optimization can be evaluated using quality indicators (QIs) assessing the quality of the approximation set generated according to convergence, spread, and uniformity...
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We compare two iterative frequency domain sub-space identification methods using nuclear norm minimization to more commonly used non-iterative methods by means of an artificially created test problem involving very no...
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ISBN:
(纸本)9781479917730
We compare two iterative frequency domain sub-space identification methods using nuclear norm minimization to more commonly used non-iterative methods by means of an artificially created test problem involving very noisy uniformly spaced frequency data. The two corresponding optimization problems are motivated and their first-order algorithmic solutions based on the alternating direction method of multipliers and the dual accelerated gradient-projection method are stated and compared.
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