A Modified Grey Wolf Optimizer Based Data Clustering Algorithm

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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WGC: Hybridization of exponential grey wolf optimizer with whale optimization for data clustering

Alexandria Engineering Journal ◽

10.1016/j.aej.2017.04.013 ◽

2018 ◽

Vol 57 (3) ◽

pp. 1569-1584 ◽

Cited By ~ 29

Author(s):

Amolkumar Narayan Jadhav ◽

N. Gomathi

Keyword(s):

Data Clustering ◽

Grey Wolf Optimizer ◽

Grey Wolf ◽

Whale Optimization

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KERNEL-BASED EXPONENTIAL GREY WOLF OPTIMIZER FOR RAPID CENTROID ESTIMATION IN DATA CLUSTERING

Jurnal Teknologi ◽

10.11113/.v78.8057 ◽

2016 ◽

Vol 78 (11) ◽

Cited By ~ 2

Author(s):

Amolkumar Narayan Jadhav ◽

Gomathi N.

Keyword(s):

Data Clustering ◽

Objective Evaluation ◽

Multidimensional Data ◽

Grey Wolf Optimizer ◽

Similar Data ◽

Grey Wolf ◽

Data Set ◽

Wide Range ◽

Centroid Estimation ◽

Two Parameters

Clustering finds variety of application in a wide range of disciplines because it is mostly helpful for grouping of similar data objects together. Due to the wide applicability, different algorithms have been presented in the literature for segmenting large multidimensional data into discernible representative clusters. Accordingly, in this paper, Kernel-based exponential grey wolf optimizer (KEGWO) is developed for rapid centroid estimation in data clustering. Here, KEGWO is newly proposed to search the cluster centroids with a new objective evaluation which considered two parameters called logarithmic kernel function and distance difference between two top clusters. Based on the new objective function and the modified KEGWO algorithm, centroids are encoded as position vectors and the optimal location is found for the final clustering. The proposed KEGWO algorithm is evaluated with banknote authentication Data Set, iris dataset and wine dataset using four metrics such as, Mean Square Error, F-measure, Rand co-efficient and jaccord coefficient. From the outcome, we proved that the proposed KEGWO algorithm outperformed the existing algorithms.

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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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Grey Wolf Optimizer Based Web usage Data Clustering with Enhanced Fuzzy C Means Algorithm

International Journal of Data Mining Techniques and Applications ◽

10.20894/ijdmta.102.006.001.003 ◽

2017 ◽

Vol 6 (1) ◽

pp. 12-16

Author(s):

P. Selvaraju ◽

◽

B. Kalaavathi ◽

Keyword(s):

Data Clustering ◽

Grey Wolf Optimizer ◽

Grey Wolf ◽

Fuzzy C Means ◽

Web Usage ◽

Fuzzy C Means Algorithm ◽

Usage Data

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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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Modified Grey Wolf Optimizer based Maximum Entropy Clustering Algorithm*

2020 International Joint Conference on Neural Networks (IJCNN) ◽

10.1109/ijcnn48605.2020.9207253 ◽

2020 ◽

Author(s):

Jia Cai ◽

Guanglong Xu ◽

Wenwen Ye

Keyword(s):

Maximum Entropy ◽

Clustering Algorithm ◽

Grey Wolf Optimizer ◽

Grey Wolf

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Grey Wolf Optimizer Based on Powell Local Optimization Method for Clustering Analysis

Discrete Dynamics in Nature and Society ◽

10.1155/2015/481360 ◽

2015 ◽

Vol 2015 ◽

pp. 1-17 ◽

Cited By ~ 25

Author(s):

Sen Zhang ◽

Yongquan Zhou

Keyword(s):

Data Clustering ◽

Optimization Problems ◽

Optimization Method ◽

Local Optimization ◽

Superior Performance ◽

Grey Wolf Optimizer ◽

Data Sets ◽

Grey Wolf ◽

Data Mining Technique ◽

Local Optimization Method

One heuristic evolutionary algorithm recently proposed is the grey wolf optimizer (GWO), inspired by the leadership hierarchy and hunting mechanism of grey wolves in nature. This paper presents an extended GWO algorithm based on Powell local optimization method, and we call it PGWO. PGWO algorithm significantly improves the original GWO in solving complex optimization problems. Clustering is a popular data analysis and data mining technique. Hence, the PGWO could be applied in solving clustering problems. In this study, first the PGWO algorithm is tested on seven benchmark functions. Second, the PGWO algorithm is used for data clustering on nine data sets. Compared to other state-of-the-art evolutionary algorithms, the results of benchmark and data clustering demonstrate the superior performance of PGWO algorithm.

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