MOBBO: A Multiobjective Brown Bear Optimization Algorithm for Solving Constrained Structural Optimization Problems

Journal of Optimization, Год журнала: 2024, Номер 2024(1)

Опубликована: Янв. 1, 2024

The multiobjective (MO) optimizers show great promise in solving constrained engineering structural problems. This paper introduces a MO version of the Brown Bear Optimization (BBO) algorithm, inspired by foraging behavior brown bears. proposed Multiobjective (MOBBO) algorithm is applied to five optimization problems, including 10‐bar, 25‐bar, 60‐bar, 72‐bar, and 942‐bar trusses, aiming minimize both mass maximum nodal deflection simultaneously. Comparative evaluations against six benchmark algorithms demonstrate MOBBO’s superior convergence, solution diversity, effectiveness addressing highly hypervolume (HV) inverted generational distance (IGD) metrics place MOBBO first rank according Friedman test, with an average standard deviation 0.0002. Moreover, spacing‐to‐extent (STE) (GD) second. final test highlights overall dominance, achieving rank. Best Pareto plots, diversity graphs, box plot analyses further suggest performance convergence compared existing algorithms. Therefore, can be effectively various tasks industry, offering refined global solutions contributing valuable insights field

Язык: Английский

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A deep learning method for differentiating safflower germplasm using optimal leaf structure features

Ecological Informatics, Год журнала: 2025, Номер unknown, С. 102998 - 102998

Опубликована: Янв. 1, 2025

Язык: Английский

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Assessment of living quality in Guangdong: A hybrid knowledge-based and data-driven approach

Ecological Informatics, Год журнала: 2024, Номер 82, С. 102745 - 102745

Опубликована: Авг. 2, 2024

Air, water, and radiation in urban living environments are crucial factors affecting human health, societal well-being, sustainable development. This study developed a novel hybrid knowledge-based data-driven approach for analyzing air, monitoring data to assess the quality of natural (ULNEQ) Guangdong Province. Fuzzy set-pair analysis was employed preprocessing, effectively incorporating domain knowledge into raw data. Then, hierarchical clustering algorithm utilized evaluate ULNEQ level. The proposed enhanced interpretability optimized process, yielding more robust reliable analytical results. results revealed diverse environmental landscape across province, with Heyuan Meizhou consistently maintaining high standards, whereas Dongguan Jieyang exhibited notably poor conditions, ranking at Level IV or V over 72% observed months. Key such as monthly average concentration ozone (0.56), city water index (0.49), proportion days standard air (PDSAQ) (−0.39) significantly influenced ULNEQ, PDSAQ showing negative correlation. Notably, province-wide observation June 2022 showed all cities ULEQ gradings II better. innovative can be adapted enhance management control ULNEQ.

Язык: Английский

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AELGNet: Attention-based Enhanced Local and Global Features Network for medicinal leaf and plant classification

Computers in Biology and Medicine, Год журнала: 2024, Номер 184, С. 109447 - 109447

Опубликована: Ноя. 28, 2024

Язык: Английский

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AyushNet – an IoT-based Mobile App for the Automatic Recognition of Medicinal Plants based on a deep residual neural network

Multimedia Tools and Applications, Год журнала: 2024, Номер unknown

Опубликована: Май 27, 2024

Язык: Английский

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