基于克隆选择算法的层叠滤波器的优化设计

层叠滤波器是一种非线性数字滤波器，具有良好的去除噪声能力和图像细节保持能力，近年来成为研究的热点问题之一．层叠滤波器设计的核心实际上是一个正布尔函数的优化问题，在MAE准则下，提出了一种基于克隆选择算法的层叠滤波器的优化方法．仿真实验结果表明，基于这种克隆选择算法的最优层叠滤波器不仅在滤波效果上好于基于遗传算法和基于粒子群算法的最优层叠滤波器，而且可以快速实现其优化过程，在较短时间内得到较好的效果，是一种有效的层叠滤波器的优化算法．

Optimizing stack filters through a clone selection algorithm

Stack filters are a class of nonlinear filters that have good performance in suppressing noises and preserving details.They have recently become the hotspot of research works.Their design is actually an optimization problem of positive Boolean function(PBF).This paper presents a clone selection algorithm for optimizing stack filters under mean absolute error(MAE) criterion.Results of experiments show that stack filters optimized with a clone selection algorithm are more effective,and optimizing speed is better than that with filters optimized by genetic algorithms or by particle swarm algorithms,which indicates that the clone selection algorithms are effective for optimizing stack filters.