共查询到17条相似文献,搜索用时 187 毫秒
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K-均值聚类具有简单、快速的特点,因此被广泛应用于图像分割领域.但K-均值聚类容易陷入局部最优,影响图像分割效果.针对K-均值的缺点,提出一种基于随机权重粒子群优化(RWPSO)和K-均值聚类的图像分割算法RWPSOK.在算法运行初期,利用随机权重粒子群优化的全局搜索能力,避免算法陷入局部最优;在算法运行后期,利用K-均值聚类的局部搜索能力,实现算法快速收敛.实验表明:RWPSOK算法能有效地克服K-均值聚类易陷入局部最优的缺点,图像分割效果得到了明显改善;与传统粒子群与K-均值聚类混合算法(PSOK)相比,RWPSOK算法具有更好的分割效果和更高的分割效率. 相似文献
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提出一种密度峰值聚类 (density peak clustering, DPC)与遗传算法(genetic algorithm, GA)相结合的新型混合算法(density peak clustering with genetic algorithm, DGA),求解带时间窗的车辆路径问题。首先应用DPC对客户进行聚类以缩减问题规模,再将聚类后的客户用GA进行线路优化。结果表明:DGA在9个数据集上的平均值比模拟退火(simulated annealing, SA)和禁忌搜索(Tabu)分别提高了13.41%和4.7%,单个数据集最大提高了26.4%。这证明了该算法是求解车辆调度问题的高效算法。 相似文献
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针对模糊C-均值聚类算法敏感于初始聚类中心及聚类收敛慢、聚类数目手动设定等缺陷,提出了基于改进蝙蝠优化自确定的模糊C-均值聚类算法。该算法是基于密度峰值综合衡量聚类中心外围数据密集程度和聚类中心间距离,自动确定聚类中心和聚类数目,以此作为改进蝙蝠算法的初始中心;在原始蝙蝠算法中引入Levy飞行特征加强算法跳出局部最优能力;使用Powell局部搜索加快算法的收敛,利用改进的蝙蝠种群进行种群寻优,并将最优蝙蝠位置作为聚类C-均值新聚类中心,进行模糊聚类,以此循环交叉迭代多次最终获得聚类结果。将基于改进蝙蝠优化自确定的模糊C-均值聚类算法与其它两种聚类算法在标准数据集上进行仿真对比,实验结果表明:与其它两种算法相比,该算法收敛速度快、误差率低。 相似文献
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K-means算法是一种常用的聚类算法,但是聚类中心的初始化是其中的一个难点。笔者提出了一个基于层次思想的初始化方法。一般聚类问题均可看作加权聚类,通过层层抽样减少数据量,然后采用自顶向下的方式,从抽样结束层到原始数据层,每层都进行聚类,其中每层初始聚类中心均通过对上层聚类中心进行换算得到,重复该过程直到原始数据层,可得原始数据层的初始聚类中心。模拟数据和真实数据的实验结果均显示基于层次抽样初始化的K-means算法不仅收敛速度快、聚类质量高,而且对噪声不敏感,其性能明显优于现有的相关算法。 相似文献
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用于彩色图像分割的改进遗传FCM算法 总被引:4,自引:0,他引:4
本文提出了一种适用于彩色图像分割的遗传模糊C均值聚类(GAFCM)算法.该算法使用Ohta等人提出的彩色特征集中的第一个分量作为图像像素的一维特征向量,并利用由像素空间到特征空间的映射来改进目标函数,从而大大降低了运算量;使用对特征空间结构没有特殊要求的特征距离代替欧氏距离,从而克服了特征空间结构对聚类结果的影响;使用引入FCM优化的遗传算法来搜索最优解,从而提高了搜索速度.实验表明,该算法不但能很好地分割彩色图像,而且具有运算量小、收敛速度快的优点. 相似文献
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考虑到模糊聚类中引入传递性可能使问题失真,提出了一类带最小约束的模糊聚类问题.给出了解决这类问题的两类方法:直接聚类法与基于无约束聚类的方法.并将这些方法与一般模糊聚类的方法进行了比较. 相似文献
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Batch processing machines that process a group of jobs simultaneously are often encountered in semiconductor manufacturing and metal heat treatment. This paper considered the problem of scheduling a batch processing machine from a clustering perspective. We first demonstrated that minimising makespan on a single batching machine with non-identical job sizes can be regarded as a special clustering problem, providing a novel insight into scheduling with batching. The definition of WRB (waste ratio of batch) was then presented, and the objective function of minimising makespan was transformed into minimising weighted WRB so as to define the distance measure between batches in a more understandable way. The equivalence of the two objective functions was also proved. In addition, a clustering algorithm CACB (constrained agglomerative clustering of batches) was proposed based on the definition of WRB. To test the effectiveness of the proposed algorithm, the results obtained from CACB were compared with those from the previous methods, including BFLPT (best-fit longest processing time) heuristic and GA (genetic algorithm). CACB outperforms BFLPT and GA especially for large-scale problems. 相似文献
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Affective design is an important aspect of new product development, especially for consumer products, to achieve a competitive edge in the marketplace. It can help companies to develop new products that can better satisfy the emotional needs of customers. However, product designers usually encounter difficulties in determining the optimal settings of the design attributes for affective design. In this article, a novel guided search genetic algorithm (GA) approach is proposed to determine the optimal design attribute settings for affective design. The optimization model formulated based on the proposed approach applied constraints and guided search operators, which were formulated based on mined rules, to guide the GA search and to achieve desirable solutions. A case study on the affective design of mobile phones was conducted to illustrate the proposed approach and validate its effectiveness. Validation tests were conducted, and the results show that the guided search GA approach outperforms the GA approach without the guided search strategy in terms of GA convergence and computational time. In addition, the guided search optimization model is capable of improving GA to generate good solutions for affective design. 相似文献
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In recent years, the volume of information in digital form has increased tremendously owing to the increased popularity of the World Wide Web. As a result, the use of techniques for extracting useful information from large collections of data, and particularly documents, has become more necessary and challenging. Text clustering is such a technique; it consists in dividing a set of text documents into clusters (groups), so that documents within the same cluster are closely related, whereas documents in different clusters are as different as possible. Clustering depends on measuring the content (i.e., words) of a document in terms of relevance. Nevertheless, as documents usually contain a large number of words, some of them may be irrelevant to the topic under consideration or redundant. This can confuse and complicate the clustering process and make it less accurate. Accordingly, feature selection methods have been employed to reduce data dimensionality by selecting the most relevant features. In this study, we developed a text document clustering optimization model using a novel genetic frog-leaping algorithm that efficiently clusters text documents based on selected features. The proposed approach is based on two metaheuristic algorithms: a genetic algorithm (GA) and a shuffled frog-leaping algorithm (SFLA). The GA performs feature selection, and the SFLA performs clustering. To evaluate its effectiveness, the proposed approach was tested on a well-known text document dataset: the “20Newsgroup” dataset from the University of California Irvine Machine Learning Repository. Overall, after multiple experiments were compared and analyzed, it was demonstrated that using the proposed algorithm on the 20Newsgroup dataset greatly facilitated text document clustering, compared with classical K-means clustering. Nevertheless, this improvement requires longer computational time. 相似文献
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Yanjuan Hu Xingfu Chang Yao Wang Zhanli Wang Chao Shi Lizhe Wu 《Materials and Manufacturing Processes》2017,32(10):1109-1115
To solve the problem of fuzzy classification of manufacturing resources in a cloud manufacturing environment, a hybrid algorithm based on genetic algorithm (GA), simulated annealing (SA) and fuzzy C-means clustering algorithm (FCM) is proposed. In this hybrid algorithm, classification is based on the processing feature and attributes of the manufacturing resource; the inner and outer layers of the nested loops are solving it, GA obtains the best classification number in the outer layer; the fitness function is constructed by fuzzy clustering algorithm (FCM), carrying out the selection, crossover and mutation operation and SA cooling operation. The final classification results are obtained in the inner layer. Using the hybrid algorithm to solve 45 kinds of manufacturing resources, the optimal classification number is 9 and the corresponding classification results are obtained, proving that the algorithm is effective. 相似文献
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S. Sreedhar Kumar Syed Thouheed Ahmed Qin Xin S. Sandeep M. Madheswaran Syed Muzamil Basha 《计算机、材料和连续体(英文)》2022,72(1):281-299
This paper presents, a new approach of Medical Image Pixels Clustering (MIPC), aims to trace the dissimilar patterns over the Magnetic Resonance (MR) image through the process of automatically identify the appropriate number of distinct clusters based on different improved unsupervised clustering schemes for enrichment, pattern predication and deeper investigation. The proposed MIPC consists of two stages: clustering and validation. In the clustering stage, the MIPC automatically identifies the distinct number of dissimilar clusters over the gray scale MR image based on three different improved unsupervised clustering schemes likely improved Limited Agglomerative Clustering (iLIAC), Dynamic Automatic Agglomerative Clustering (DAAC) and Optimum N-Means (ONM). In the second stage, the performance of MIPC approach is estimated by measuring Intra intimacy and Intra contrast of each individual cluster in the result of MR image based on proposed validation method namely Shreekum Intra Cluster Measure (SICM). Experimental results show that the MIPC approach is better suited for automatic identification of highly relative dissimilar clusters over the MR cancer images with higher Intra closeness and lower Intra contrast based on improved unsupervised clustering schemes. 相似文献
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说话人分段聚类的任务是将一段语音中由同一说话人发出的语音聚合起来。文中提出了一种基于交叉对数似然度(Cross Log-likelihood Ratio,CLR)和贝叶斯信息判据(Bayesian information criterion,BIC)相结合的说话人聚类算法。交叉对数似然度用于计算语音段间的相似度;而贝叶斯判据则提供了一种比较适当的停止聚类的准则,该算法结合了两种方法的优点,在无监督说话人聚类中得到了较好的应用。实验结果表明,基于交叉对数似然度和贝叶斯判据的说话人聚类方法,比单纯利用交叉对数似然度的方法准确度高。 相似文献