【摘要】 提出一种基于改进双种群水母搜索（Improved Double Population Jellyfish Search, IDPJS）算法的多阈值图像分割法,以解决随着阈值数目的增加,传统的图像分割计算量呈指数级增长,分割时间消耗多的问题.首先,初始化两个水母种群P₁和P₂,执行基本的JS算法.在P₁中引入组合变异策略,两个种群进行交流学习以提高算法的收敛速度.接着,对当前最好解采用动态反向学习策略,防止算法陷入局部最优.其次,利用CEC2017基准函数对所提IDPJS算法进行测试,并与5种启发式算法进行比较,实验结果显示,所提算法精度高、稳定性好.最后,将其用于多阈值图像分割问题,分别在阈值个数为5, 7, 9的情况下进行测试实验,实验表明, IDPJS算法是解决多阈值图像分割问题的有效方法.更多还原

【Abstract】 Aiming at problem of solving the multi-threshold image segmentation which computational cost of traditional image segmentation increases exponentially as the segmentation level increases, this paper proposes a multi-threshold image segmentation method based on an improved double-population jellyfish search（IDPJS） algorithm. Firstly, initialize two jellyfish populations P₁ and P₂. Perform basic JS algorithm. The combined mutation strategy is introduced into P₁, and the two populations share information by interactive learning to improve the convergence speed of the algorithm. The dynamic opposite learning strategy is used for the current best solution to prevent the algorithm from falling into the local optima. Secondly, the IDPJS algorithm is validated on CEC2017 benchmark functions,and it is compared with five heuristic algorithms. The experimental results show that the proposed algorithm has high precision and good stability. Finally, the IDPJS algorithm is applied to the multithreshold image segmentation. Image segmentation tests are carried out at threshold levels of 5, 7,and 9, respectively. The results show that the proposed algorithm is an effective method to solve the multi-threshold image segmentation problem.更多还原