Abstract: Aiming at the disadvantages of shuffled frog leaping algorithm (SFLA),such as high computational comple-xity 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.

Key words: Image segmentation, Intelligent optimization algorithm, Multi-threshold ima-ge segmentation, Renyi entropy, Shuffled frog leaping algorithm