基于分数阶达尔文粒子群FODPSO算法的图像分割

图像分割主要用于提取用户感兴趣的目标,是图像分类和识别的基础。采用一种基于分数阶达尔文粒子群算法的图像分割方法,该算法采用分数阶微积分控制系统收敛性,能够对n尺度图像进行n-1个阈值寻优计算。实验结果表明,对比于APSO、CFPSO算法,该算法具有收敛速度快、稳定性强、精度高、全局寻优等特点,有效地克服了传统算法易陷入局部最优和收敛速度慢等缺陷,可满足实际工程需求。

Image segmentation based on fractional-order Darwinian particle swarm optimization

Image segmentation mainly extracts the objectives users are interested in, and it is the basis for image classification and pattern recognition. We present a novel image segmentation method based on fractional-order Darwinian particle swarm optimization, called FODPSO . The algorithm utilizes the fractional calculus strategy to control the convergence of particles and is able to determine the n-1 optimal for n-level threshold on a given image. Compared with the APSO and the CFPSO algorithms, testing results show that the FODPSO algorithm can enhance the performance in terms of convergence speed, stability, solution accuracy and global optimality, and greatly overcome the shortcomings of traditional methods, such as local optima and slow convergence speed. Hence, the FODPSO is applicable to practical projects.