DETDO: An adaptive hybrid dandelion optimizer for engineering optimization

Hu, Gang; Zheng, Yanfang; Abualigah, Laith; Hussien, Abdelazim G.

doi:10.1016/j.aei.2023.102004

Cited by 124 publications

(14 citation statements)

References 70 publications

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“…The question of how to simplify or build the mathematical model in a simple way so that it can be applied to the approximate merging of disk curves is a problem to be solved in the future. In addition, BEESO can be used to solve optimization problems in areas such as cryptosystem design [ 63 ], path planning, geometry optimization [ 40 , 64 ], engineering design [ 65 , 66 ], and feature selection [ 67 ].…”

Section: Discussionmentioning

confidence: 99%

Ameliorated Snake Optimizer-Based Approximate Merging of Disk Wang–Ball Curves

Lu,

Yang,

et al. 2024

Biomimetics

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A method for the approximate merging of disk Wang–Ball (DWB) curves based on the modified snake optimizer (BEESO) is proposed in this paper to address the problem of difficulties in the merging of DWB curves. By extending the approximate merging problem for traditional curves to disk curves and viewing it as an optimization problem, an approximate merging model is established to minimize the merging error through an error formulation. Considering the complexity of the model built, a BEESO with better convergence accuracy and convergence speed is introduced, which combines the snake optimizer (SO) and three strategies including bi-directional search, evolutionary population dynamics, and elite opposition-based learning. The merging results and merging errors of numerical examples demonstrate that BEESO is effective in solving approximate merging models, and it provides a new method for the compression and transfer of product shape data in Computer-Aided Geometric Design.

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Section: Discussionmentioning

confidence: 99%

Ameliorated Snake Optimizer-Based Approximate Merging of Disk Wang–Ball Curves

Lu,

Yang,

et al. 2024

Biomimetics

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“…Further, potential future application to exercises or movements where, for example, the elbow angles and hand positions are of importance will require additional training and testing. In the future, application of recent novel optimization strategies (such as [ [34] , [35] , [36] , [37] , [38] , [39] ]) from related areas will likely further improve the quality of human pose prediction from video.…”

Section: Discussionmentioning

confidence: 99%

Exercise quantification from single camera view markerless 3D pose estimation

Mercadal-Baudart,

Liu,

Farrell

et al. 2024

Heliyon

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“…In the future, Authors can use many optimization algorithms and embedded them in the network for better accuracy. These algorithms can be any algorithm such as Snake Optimizer (SO) 45 , Fick’s Law Algorithm (FLA) 46 , Jellyfish Search (JS) 47 , Dandelion Optimizer (DO) 48 , Aquila Optimizer 49 – 51 , Atom Search Optimization (ASO) 52 , Water Cycle Algorithm (WCA) 53 , Bald Eagle Search (BES) 54 , African Vultures Optimization Algorithm (AVOA) 55 , Archimedes Optimization Algorithm (AOA) 56 , Beluga Whale Optimization (BWO) 57 , Hunter Prey Optimization (HPO) 58 , INFO 59 , Supply Demand Optimizer 60 , 61 , Reptile Search Algorithm (RSA) 62 , Golden Jackle Optimization (GJO) 63 , and more.…”

Section: Discussionmentioning

confidence: 99%

A CNN-based model to count the leaves of rosette plants (LC-Net)

Deb,

Dhal,

Das

et al. 2024

Sci Rep

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Plant image analysis is a significant tool for plant phenotyping. Image analysis has been used to assess plant trails, forecast plant growth, and offer geographical information about images. The area segmentation and counting of the leaf is a major component of plant phenotyping, which can be used to measure the growth of the plant. Therefore, this paper developed a convolutional neural network-based leaf counting model called LC-Net. The original plant image and segmented leaf parts are fed as input because the segmented leaf part provides additional information to the proposed LC-Net. The well-known SegNet model has been utilised to obtain segmented leaf parts because it outperforms four other popular Convolutional Neural Network (CNN) models, namely DeepLab V3+, Fast FCN with Pyramid Scene Parsing (PSP), U-Net, and Refine Net. The proposed LC-Net is compared to the other recent CNN-based leaf counting models over the combined Computer Vision Problems in Plant Phenotyping (CVPPP) and KOMATSUNA datasets. The subjective and numerical evaluations of the experimental results demonstrate the superiority of the LC-Net to other tested models.

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DETDO: An adaptive hybrid dandelion optimizer for engineering optimization

Cited by 124 publications

References 70 publications

Ameliorated Snake Optimizer-Based Approximate Merging of Disk Wang–Ball Curves

Ameliorated Snake Optimizer-Based Approximate Merging of Disk Wang–Ball Curves

Exercise quantification from single camera view markerless 3D pose estimation

A CNN-based model to count the leaves of rosette plants (LC-Net)

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