改进的混合蛙跳算法及其在多阈值图像分割中的应用

Improved Shuffled Frog Leaping Algorithm and Its Application in Multi-threshold Image Segmentation

针对混合蛙跳算法(Shuffled Frog Leaping Algorithm,SFLA)存在的计算复杂度高、优化效率不理想等问题,提出了一种改进的混合蛙跳算法(Improved Shuffled Frog Leaping Algorithm,ISFLA)。在原始SFLA的基础上进行如下改进:首先,将其中每次只更新组内最差青蛙的方式改为更新组内所有青蛙的方式,这既增大了获得优质解的概率,又省去了调整组内迭代次数的步骤,从而提升了优化效率和可操作性;其次,将基于局部最优更新的方法和基于全局最优更新的方法融合为一种混合扰动更新方法,从而避免了复杂条件的选择步骤,进一步提升了优化效率;最后,去掉随机更新方式,以免优质解被破坏,从而提高了整体的优化性能。将ISFLA用于CEC2005和CEC2015连续基准函数的优化测试和基于Renyi熵的灰度和彩色图像分割的多阈值选择实验中,结果表明,与SFLA和state-of-the-art的LSFLA相比,ISFLA具有更高的优化效率,更适用于多阈值图像分割的阈值选择。

Aiming at the disadvantages of shuffled frog leaping algorithm（SFLA）,such as high computational complexity and poor optimization efficiency,an improved shuffled frog leaping algorithm（ISFLA）was proposed in this paper.The following improvements have been made on the basis of SFLA.Firstly,the method which only updates the worst frog in SFLA is replaced by the method which updates all frogs in each group.This replacement can increase the probability of obtaining the high quality solutions,omit the steps of setting the number of iterations in the group and then improve the optimization efficiency and operability.Secondly,the method based on local optimum updating and the method based on global optimum updating are combined into a hybrid disturbance updating method,which avoids the tedious condition selection steps and further improves the optimization efficiency.Finally,the random updating method is removed to avoid destroying the superior solutions and further enhance the overall performance optimization.ISFLA was tested on the benchmark functions from CEC2005 and CEC2015,and was applied to the multi-threshold gray and color images segmentation based on Renyi entropy.The experimental results show that,ISFLA obtains higher optimization efficiency and is more suitable for threshold selection of multi-threshold image segmentation compared with SFLA and the state-of-the-art LSFLA.