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Objects Extraction and Classification Based on an Improved Cat Swarm Optimization Algorithm

机译:基于改进的猫群优化算法的目标提取与分类

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The convergence time of some traditional swarm-intelligence-based algorithms are often overly long when they are used to extract and classify objects in an image. Therefore, an improved cat swarm optimization algorithm is proposed to solve the problem in this paper. In order to improve the convergence speed of the cat swarm optimization algorithm, the velocity updating formula is modified with a nonlinear inertia weight and linear acceleration coefficients. Simultaneously, in order to improve the search speed, the elite reserve strategy is used in the new algorithm. Experiments show that the improved cat swarm optimization algorithm is more effective than other traditional swarm-intelligence-based algorithms when it is used to extract and classify objects in an image.
机译:当一些传统的基于群智能的算法用于图像中的对象提取和分类时,其收敛时间通常会过长。因此,提出了一种改进的猫群优化算法来解决该问题。为了提高猫群优化算法的收敛速度,采用非线性惯性权重和线性加速度系数对速度更新公式进行了修改。同时,为了提高搜索速度,新算法采用了精英后备策略。实验表明,改进的猫群优化算法用于提取和分类图像中的对象时,比其他传统的基于群智能的算法更有效。

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