基于改进MOJAYA/D算法的图像分割  

An Improved MOJAYA/D Algorithm for Image Segmentation

作　　者：刘辉 邹锋[1] 陈得宝 籍旭颖 张妍 LIU Hui;ZOU Feng;CHEN De-bao;JI Xu-ying;ZHANG Yan(School of Physics and Electronic Information,Huaibei Normal University,Huaibei 235000,China;School of Computer Science and Technology,Huaibei Normal University,Huaibei 235000,China)

机构地区：[1]淮北师范大学物理与电子信息学院,安徽淮北235000 [2]淮北师范大学计算机科学与技术学院,安徽淮北235000

出　　处：《计算机与现代化》2023年第4期62-72,共11页Computer and Modernization

基　　金：国家自然科学基金资助项目(61976101);安徽省学术和技术带头人及后备人选学术科研活动经费资助项目(2021H264)。

摘　　要：为处理多目标优化问题,提出一种基于分解的多目标JAYA(MOJAYA/D)算法。该算法在原始基于分解的多目标算法的基础上,将JAYA算法延伸至多目标优化领域;同时,引入Lévy飞行策略增强算法的扰动,并且增加一个反馈学习阶段来提高个体的学习能力,使得算法的多样性和全局寻优的水平得到提高。为了验证提出算法的性能,将该算法在ZDT和DTLZ测试函数上与几个经典的多目标算法进行对比。实验结果表明,MOJAYA/D在收敛性和多样性方面都优于其他比较算法。最后,将该算法应用于多个目标准则下的图像分割问题。分割结果表明,MOJAYA/D在处理图像分割问题上效果显著。To deal with the multi-objective optimization problem,the multi-objective JAYA algorithm based on decomposition is proposed.Based on the decomposition-based multi-objective algorithm,the JAYA algorithm is extended to the multi-objective optimization field.Meanwhile,a Lévy flight strategy is introduced to enhance the perturbation of the algorithm,and a feedback learning phase is added to improve the individual learning ability,resulting in the improvement of the algorithm’s diversity and the ability of global optimization search.To verify the performance of the proposed algorithm,it is compared with several classical multi-objective algorithms on the ZDT and DTLZ test functions.The results show that MOJAYA/D outperforms the other algorithms in both convergence and diversity.Finally,the proposed algorithm is applied to the image segmentation problem under multiple objective criteria.The segmentation results show that MOJAYA/D is very effective in dealing with image segmentation problems.

关 键 词：JAYA算法 多目标优化 反馈学习 图像分割 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]