A Grey Wolf-Based Clustering Algorithm for Medical Diagnosis Problems

The widespread application of clustering in various fields leads to the discovery of different clustering techniques in order to partition multidimensional data into separable clusters. Although there are various clustering approaches used in literature, optimized clustering techniques with multi-objective consideration are rare. This paper proposes a novel data clustering algorithm, Enhanced Kernel-based Exponential Grey Wolf Optimization (EKEGWO), handling two objectives. EKEGWO, which is the extension of KEGWO, adopts weight exponential functions to improve the searching process of clustering. Moreover, the fitness function of the algorithm includes intra-cluster distance and the inter-cluster distance as an objective to provide an optimum selection of cluster centroids. The performance of the proposed technique is evaluated by comparing with the existing approaches PSC, mPSC, GWO, and EGWO for two datasets: banknote authentication and iris. Four metrics, Mean Square Error (MSE), F-measure, rand and jaccord coefficient, estimates the clustering efficiency of the algorithm. The proposed EKEGWO algorithm can attain an MSE of 837, F-measure of 0.9657, rand coefficient of 0.8472, jaccord coefficient of 0.7812, for the banknote dataset.

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Routing Protocol for Heterogeneous Wireless Sensor Networks Based on a Modified Grey Wolf Optimizer

Sensors ◽

10.3390/s20030820 ◽

2020 ◽

Vol 20 (3) ◽

pp. 820 ◽

Cited By ~ 3

Author(s):

Xiaoqiang Zhao ◽

Shaoya Ren ◽

Heng Quan ◽

Qiang Gao

Keyword(s):

Routing Protocol ◽

Clustering Algorithm ◽

Wireless Sensor ◽

Grey Wolf Optimizer ◽

Grey Wolf ◽

Fitness Value ◽

Heterogeneous Wireless Sensor Networks ◽

Limited Power ◽

Energy Efficient Clustering ◽

Node Energy

Wireless sensor network (WSN) nodes are devices with limited power, and rational utilization of node energy and prolonging the network lifetime are the main objectives of the WSN’s routing protocol. However, irrational considerations of heterogeneity of node energy will lead to an energy imbalance between nodes in heterogeneous WSNs (HWSNs). Therefore, in this paper, a routing protocol for HWSNs based on the modified grey wolf optimizer (HMGWO) is proposed. First, the protocol selects the appropriate initial clusters by defining different fitness functions for heterogeneous energy nodes; the nodes’ fitness values are then calculated and treated as initial weights in the GWO. At the same time, the weights are dynamically updated according to the distance between the wolves and their prey and coefficient vectors to improve the GWO’s optimization ability and ensure the selection of the optimal cluster heads (CHs). The experimental results indicate that the network lifecycle of the HMGWO protocol improves by 55.7%, 31.9%, 46.3%, and 27.0%, respectively, compared with the stable election protocol (SEP), distributed energy-efficient clustering algorithm (DEEC), modified SEP (M-SEP), and fitness-value-based improved GWO (FIGWO) protocols. In terms of the power consumption and network throughput, the HMGWO is also superior to other protocols.

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A Feature Weighted Fuzzy Clustering Algorithm Based on Multistrategy Grey Wolf Optimization

Journal of Electrical and Computer Engineering ◽

10.1155/2021/7387153 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Yongli Liu ◽

Zhonghui Wang ◽

Hao Chao

Keyword(s):

Fuzzy Clustering ◽

Clustering Algorithm ◽

Learning Strategy ◽

Optimal Solution ◽

Population Diversity ◽

Grey Wolf ◽

Grey Wolf Optimization ◽

Opposition Based Learning ◽

Fuzzy Clustering Algorithm ◽

The Impact

Traditional fuzzy clustering is sensitive to initialization and ignores the importance difference between features, so the performance is not satisfactory. In order to improve clustering robustness and accuracy, in this paper, a feature-weighted fuzzy clustering algorithm based on multistrategy grey wolf optimization is proposed. This algorithm cannot only improve clustering accuracy by considering the different importance of features and assigning each feature different weight but also can easily obtain the global optimal solution and avoid the impact of the initialization process by implementing multistrategy grey wolf optimization. This multistrategy optimization includes three components, a population diversity initialization strategy, a nonlinear adjustment strategy of the convergence factor, and a generalized opposition-based learning strategy. They can enhance the population diversity, better balance exploration and exploitation, and further enhance the global search capability, respectively. In order to evaluate the clustering performance of our clustering algorithm, UCI datasets are selected for experiments. Experimental results show that this algorithm can achieve higher accuracy and stronger robustness.

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Multiple fuzzy c-means clustering algorithm in medical diagnosis

Technology and Health Care ◽

10.3233/thc-150989 ◽

2015 ◽

Vol 23 (s2) ◽

pp. S519-S527 ◽

Cited By ~ 4

Author(s):

Yanping Wu ◽

Huilong Duan ◽

Shufeng Du

Keyword(s):

Medical Diagnosis ◽

Clustering Algorithm ◽

Fuzzy C Means ◽

Fuzzy C Means Clustering

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A Comparative Analysis of Depth Computation of Leukaemia Images using a Refined Bit Plane and Uncertainty Based Clustering Techniques

Cybernetics and Information Technologies ◽

10.1515/cait-2015-0011 ◽

2015 ◽

Vol 15 (1) ◽

pp. 126-146

Author(s):

Swarnalatha Purushotham ◽

B. K. Tripathy

Keyword(s):

Image Segmentation ◽

Comparative Analysis ◽

Medical Diagnosis ◽

Clustering Algorithm ◽

Time Estimation ◽

The Other ◽

Clustering Techniques ◽

Computer Aided ◽

Bit Plane ◽

Aided Diagnosis

Abstract Several image segmentation techniques have been developed over the years to analyze the characteristics of images. Among these, the uncertainty based approaches and their hybrids have been found to be more efficient than the conventional and individual ones. Very recently, a hybrid clustering algorithm, called Rough Intuitionistic Fuzzy C-Means (RIFCM) was proposed by the authors and proved to be more efficient than the conventional and other algorithms applied in this direction, using various datasets. Besides, in order to remove noise from the images, a Refined Bit Plane (RBP) algorithm was introduced by us. In this paper we use a combination of the RBP and RIFCM to propose an approach and apply it to leukemia images. The aim of the paper is twofold. First, it establishes the superiority of our approach in medical diagnosis in comparison to most of the conventional, as well as uncertainty based approaches. The other objective is to provide a computer-aided diagnosis system that will assist the doctors in evaluating medical images in general, and also in easy and better assessment of the disease in leukaemia patients. We have applied several measures like DB-index, D-index, RMSE, PSNR, time estimation in depth computation and histogram analysis to support our conclusions.

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A new K-means grey wolf algorithm for engineering problems

World Journal of Engineering ◽

10.1108/wje-10-2020-0527 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Hardi M. Mohammed ◽

Zrar Kh. Abdul ◽

Tarik A. Rashid ◽

Abeer Alsadoon ◽

Nebojsa Bacanin

Keyword(s):

Clustering Algorithm ◽

Heuristic Algorithms ◽

Bat Algorithm ◽

Engineering Problem ◽

Gray Wolf ◽

Grey Wolf ◽

Content Type ◽

Gray Wolves ◽

Whale Optimization ◽

Benchmark Test Functions

Purpose This paper aims at studying meta-heuristic algorithms. One of the common meta-heuristic optimization algorithms is called grey wolf optimization (GWO). The key aim is to enhance the limitations of the wolves’ searching process of attacking gray wolves. Design/methodology/approach The development of meta-heuristic algorithms has increased by researchers to use them extensively in the field of business, science and engineering. In this paper, the K-means clustering algorithm is used to enhance the performance of the original GWO; the new algorithm is called K-means clustering gray wolf optimization (KMGWO). Findings Results illustrate the efficiency of KMGWO against to the GWO. To evaluate the performance of the KMGWO, KMGWO applied to solve CEC2019 benchmark test functions. Originality/value Results prove that KMGWO is superior to GWO. KMGWO is also compared to cat swarm optimization (CSO), whale optimization algorithm-bat algorithm (WOA-BAT), WOA and GWO so KMGWO achieved the first rank in terms of performance. In addition, the KMGWO is used to solve a classical engineering problem and it is superior.

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A Grey Wolf Optimizer for Text Document Clustering

Journal of Intelligent Systems ◽

10.1515/jisys-2018-0194 ◽

2018 ◽

Vol 29 (1) ◽

pp. 814-830 ◽

Cited By ~ 11

Author(s):

Hasan Rashaideh ◽

Ahmad Sawaie ◽

Mohammed Azmi Al-Betar ◽

Laith Mohammad Abualigah ◽

Mohammed M. Al-laham ◽

...

Keyword(s):

Clustering Algorithm ◽

Document Clustering ◽

Text Clustering ◽

The Other ◽

Grey Wolf Optimizer ◽

Data Sets ◽

Complex Data ◽

Grey Wolf ◽

Clustering Methods ◽

F Measure

Abstract Text clustering problem (TCP) is a leading process in many key areas such as information retrieval, text mining, and natural language processing. This presents the need for a potent document clustering algorithm that can be used effectively to navigate, summarize, and arrange information to congregate large data sets. This paper encompasses an adaptation of the grey wolf optimizer (GWO) for TCP, referred to as TCP-GWO. The TCP demands a degree of accuracy beyond that which is possible with metaheuristic swarm-based algorithms. The main issue to be addressed is how to split text documents on the basis of GWO into homogeneous clusters that are sufficiently precise and functional. Specifically, TCP-GWO, or referred to as the document clustering algorithm, used the average distance of documents to the cluster centroid (ADDC) as an objective function to repeatedly optimize the distance between the clusters of the documents. The accuracy and efficiency of the proposed TCP-GWO was demonstrated on a sufficiently large number of documents of variable sizes, documents that were randomly selected from a set of six publicly available data sets. Documents of high complexity were also included in the evaluation process to assess the recall detection rate of the document clustering algorithm. The experimental results for a test set of over a part of 1300 documents showed that failure to correctly cluster a document occurred in less than 20% of cases with a recall rate of more than 65% for a highly complex data set. The high F-measure rate and ability to cluster documents in an effective manner are important advances resulting from this research. The proposed TCP-GWO method was compared to the other well-established text clustering methods using randomly selected data sets. Interestingly, TCP-GWO outperforms the comparative methods in terms of precision, recall, and F-measure rates. In a nutshell, the results illustrate that the proposed TCP-GWO is able to excel compared to the other comparative clustering methods in terms of measurement criteria, whereby more than 55% of the documents were correctly clustered with a high level of accuracy.

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A Modified Grey Wolf Optimizer Based Data Clustering Algorithm

Applied Artificial Intelligence ◽

10.1080/08839514.2020.1842109 ◽

2020 ◽

Vol 35 (1) ◽

pp. 63-79

Author(s):

Ramin Ahmadi ◽

Gholamhossein Ekbatanifard ◽

Peyman Bayat

Keyword(s):

Data Clustering ◽

Clustering Algorithm ◽

Grey Wolf Optimizer ◽

Grey Wolf

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