To address the imperative of lowering energy generation costs in offshore wind farms, advanced optimization technologies are crucial. This study present multimodal method in optimizing the cabling cost. By leveraging ...
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ISBN:
(纸本)9798350349047;9798350349030
To address the imperative of lowering energy generation costs in offshore wind farms, advanced optimization technologies are crucial. This study present multimodal method in optimizing the cabling cost. By leveraging the nature of species formation in multimodal algorithms, the proposed Nearest Better Neighbor Clustering-Particle Swarm Optimization (NBNC-PSO) method yields a number of feasible solutions. The proposed method calculates and optimize cable-related costs (investment, energy loss, and construction), cable selection, connections, and substation locations. This enhances the adaptability of offshore wind farm designs with algorithm parameter. The proposed modality reduces the total costs by 14.42%, and further adjustment in the parameter of algorithm results in 15.6% reduction in costs.
multimodal optimization problems (MMOPs) require the identification of multiple optimal solutions for decision makers. To address MMOPs, algorithms must enhance the population diversity to find more global optimal reg...
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multimodal optimization problems (MMOPs) require the identification of multiple optimal solutions for decision makers. To address MMOPs, algorithms must enhance the population diversity to find more global optimal regions while simultaneously refine the solution accuracy on each optimum. Therefore, in this paper, we introduces a bi-stage learning differential evolution (BLDE) with two learning stages: the pre-learning Find stage and the post-learning Refine stage. First of all, a bi-stage learning niching technique (BLNT) is proposed, which forms wide niches for full exploration in the pre-learning Find stage, while adaptively adjusts the niche radius for each individual to refine its corresponding solution accuracy in the post-learning Refine stage. Subsequently, a bi-stage learning mutation strategy (BLMS) is developed, enabling each individual to adaptively choose the suitable mutation strategy, achieving effective guidance for evolution. Moreover, different from other DE-based multimodal algorithms with only one selection operator, a bi-stage learning selection strategy (BLSS) is proposed to determine the suitable selection operator in different learning stages and preserve the promising individuals. The widely-used multimodal benchmark functions from CEC2015 competition are employed to evaluate the performance of BLDE. The results demonstrate that BLDE generally outperforms or at least comparable with other state-of-the-art multimodal algorithms, including the champion of CEC2015 competition. Moreover, BLDE is further applied to the real-world multimodal nonlinear equation system (NES) problems to demonstrate its applicability.
This paper presents a novel technique for story segmentation of news videos. The visual similarity, silence in the audio and the text in text boxes of a news video are used as parameters to define the story boundaries...
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This paper presents a novel technique for story segmentation of news videos. The visual similarity, silence in the audio and the text in text boxes of a news video are used as parameters to define the story boundaries. Each of these parameters is used to create an index and these three indices are fed to a probabilistic multimodal algorithm which then predicts the story breaks. The multimodal algorithm takes account of the previous state of the indices and predicts the present state. It is then compared with the actual present indices and story breaks are determined. The segmented stories are then indexed for easy retrieval of the stories.
In recent years, surrogate models have gained popularity as a tool to tackle the challenges presented by timeconsuming numerical simulations in automatic history matching (AHM). Although there are many different surro...
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In recent years, surrogate models have gained popularity as a tool to tackle the challenges presented by timeconsuming numerical simulations in automatic history matching (AHM). Although there are many different surrogate models designed to alleviate this issue, it is still challenging for most of them to handle the high dimensionality and strong non-linearity in reservoir models and dynamic production data. Inspired by the rapid development of Transformer, we propose a novel hybrid hierarchical Vision Transformer (HHVT) approach for history matching, which utilizes a unified architecture to predict production data for specific physical fields with an end-to-end strategy. For predicting the production data of wells, the Transformer supports parallelism computation among multiple timesteps, which shows more superiority than traditional recurrent neural networks. Specifically, our approach constructs a novel encoder-decoder Transformer architecture to learn the implicit features of high-level spatial parameters to match the features of time-series production data. With this architecture, HHVT achieves fast training and inference, which is suitable for large-scale datasets and highdimensional features. The proposed HHVT model is integrated with a multimodal optimization algorithm to find history-matching solutions. We first validated the effect of hyperparameters of HHVT on a simple 2D reservoir. Moreover, the proposed method was verified on the complex 3D Brugge model. The results demonstrate that the training speed of the Transformer-based model is approximately twice as fast as the surrogates based on convolutional and recurrent neural networks. The proposed HHVT also shows better prediction accuracy in two cases, compared with other surrogate models, which enhances the applicability of surrogate-based history-matching methods in large-scale complex reservoir scenarios.
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