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Arabian Journal for Science and Engineering
Erschienen in: Arabian Journal for Science and Engineering 11/2019

02.05.2019 | Research Article - Computer Engineering and Computer Science

Improved Hybrid Bat Algorithm with Invasive Weed and Its Application in Image Segmentation

verfasst von: Xiaofeng Yue, Hongbo Zhang

Erschienen in: Arabian Journal for Science and Engineering | Ausgabe 11/2019

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Abstract

As one of the most popular and effective image segmentation methods, multi-level thresholding is widely used. However, too much computation is needed to select the optimal thresholds with basic ergodic method. In order to solve this problem, a hybrid bat algorithm (IWBA) which incorporates bat algorithm with invasive weed optimization (IWO) is employed to choose the optimal thresholds. In IWBA algorithm, the local search ability is enhanced by integrating with IWO algorithm. Furthermore, a new inertia weight based on Lagrange interpolation is proposed to balance exploration and exploitation. In IWBA algorithm, scale parameter of normal distribution is adjusted according to the value of fitness. It is established that IWBA algorithm is able to segment the image in more efficient and accurate way than other algorithms. More importantly, IWBA algorithm can also be applied to other fields.
Vorheriger Artikel Hybrid Cascade Forward Neural Network with Elman Neural Network for Disease Prediction
Nächster Artikel AE-MCCF: An Autoencoder-Based Multi-criteria Recommendation Algorithm

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Metadaten
Titel
Improved Hybrid Bat Algorithm with Invasive Weed and Its Application in Image Segmentation
verfasst von
Xiaofeng Yue
Hongbo Zhang
Publikationsdatum
02.05.2019
Verlag
Springer Berlin Heidelberg
Erschienen in
Arabian Journal for Science and Engineering / Ausgabe 11/2019
Print ISSN: 2193-567X
Elektronische ISSN: 2191-4281
DOI
https://doi.org/10.1007/s13369-019-03874-y

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