步长自适应果蝇算法控制工程.docVIP

文章编号：1671-7848(2016)04-0000-06 DOI: 10.14107/j.cnki.kzgc.150427 果蝇算法优化模糊均值聚类算法的图像分割 xxx1a，，， (1.xx学校 a. xx学院；b. xx学院，；.xx学校 xx学院，；.xx学校 xx学院，北京) 摘 要：为提高图像分割精度，并针对传统模糊均值聚类算法存在的聚类中心选取问题，提出一种改进果蝇算法优化模糊均值聚类算法的图像分割算法。首先，根据上一代最优味道浓度判断值和当前迭代次数来自适应调整果蝇算法进化步长，在初期的步长大可避免陷入局部最优，进化后期果蝇移动步长变小可获得更高的收敛精度，加快收敛速度；然后，采用改进果蝇算法选择模糊均值聚类算法的初始聚类中心，实现图像分割；最后，采用仿真实验测试算法的性能，实验结果表明，相对于对比算法，算法在分割正确率、分割速度及鲁棒性上均更优，具有更广的应用前景。（不包含“本文”字样） 关键词：图像分割；模糊均值聚类算法；果蝇算法；味道浓度 中图分类号：TP18 文献标识码：A Image Segmentation Based on Self-adaptive Fruit Fly Algorithm Optimizing Fuzzy Means Clustering AlgorithmLI Xin-sun1a, XU Zhi-zhang1b, XIAN Rui-deng2, WEI Ping3 (1. a. College of Engineering; b. Business school, Yantai Nanshan University, Yantai 265713, China; 2. Department of Computer Science, Tangshan Normal University, Tangshan 063000, China; 3. College of Geophysics and Information Engineering, China University of Petroleum, Beijing 102249, China) Abstract: In order to improve the image segmentation accuracy, and solve the problems in the traditional fuzzy clustering algorithm, this paper puts forward an image segmentation algorithm based on improved fruit fly algorithm optimizing fuzzy means clustering algorithm. Firstly, the evolutionary step of the fruit fly algorithm is adjusted according to the best flavor value and the current iteration number, which can avoid falling into local optimum, obtain higher convergence precision, and accelerate the convergence speed. Then, the initial cluster centers of fuzzy k-means clustering algorithm are selected by using the improved fruit fly algorithm to realize image segmentation. Finally, the performance is tested by simulation experiments. The experimental results show that, compared with other images segmentation algorithms, the proposed algorithm is better in segmentation accuracy rate, segmentation speed and robustness, and has wider application scope. Key words: Image segmentation; fuzzy means clustering algorithm; fruit fly algorithm; taste concentration 1 引 言 图像