.png){kind=icon}
.png){kind=icon}
Return
A Scheme of Color Image Multithreshold Segmentation Based on Improved Moth-Flame Algorithm
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2020.3025833.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
A recently developed swarm intelligence algorithm by studying the natural moth's biological behavior is called Moth-Flame Optimization (MFO). The advantages of MFO conclude a simple structure and a robust selection capability. Still, it is easy to be trapped falling into optimal local, and slow search converges. This study suggests a new process improving MFO by hybridizing Levy flight and logarithmic functions for its formula of flame updating to enhance the optimization performance of the algorithm. In the experimental section, a set of benchmark functions of CEC2013 and the multi threshold image segmentation are used to evaluate the proposed method performance. Compared results of the proposed methods with the different algorithms in the same condition scenarios show that the suggested approach provides better results than the various algorithms in the competitions.
Keywords:
Image segmentation
Fires
Optimization
Color
Particle swarm optimization
Linear programming
Licenses
Moth-flame algorithm
color image segmentation
multi threshold segmentation
minimum cross-entropy
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.