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

  • Research Article - Computer Engineering and Computer Science
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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.

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Authors and Affiliations

  1. School of Mechatronic Engineering, Changchun University of Technology, Changchun, 130012, People’s Republic of China

    **aofeng Yue & Hongbo Zhang

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  1. **aofeng Yue
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  2. Hongbo Zhang
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Correspondence to **aofeng Yue.

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Yue, X., Zhang, H. Improved Hybrid Bat Algorithm with Invasive Weed and Its Application in Image Segmentation. Arab J Sci Eng 44, 9221–9234 (2019). https://doi.org/10.1007/s13369-019-03874-y

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