Summary The energy efficiency sustenance in wireless sensor networks completely depends on the process of clustering to prolong network lifetime. The success of this clustering process completely depends on the cluster head (CH) selection process as potential CH selection prevents frequent clustering and aids in maintaining maximized energy in the network. In this paper, an improved Game Theory‐Gray Wolf Optimization algorithm (GAGWO)‐based efficient CH selection strategy is improved for maintaining the energy in the network through the construction of equal size clusters. This GWOA mimicked the hunting and attacking characteristics of gray wolves and facilitated better CH selection, which completely prevented worst sensor nodes from being selected as CH. It is proposed for establishing a better trade‐off between the local and global search for the objective of preventing re‐clustering that unnecessarily drains network energy. The simulation results of the proposed GAGWO confirmed better performance in terms of network lifetime, energy stability, mean throughput, and packet delivery ratio, on an average by 23.18%, 21.86%, 19.84%, and 20.98%, compared to the baseline CH approaches used for investigation.
Cluster analysis plays a foremost role in identifying groups of genes that show similar behavior under a set of experimental conditions. Several clustering algorithms have been proposed for identifying gene behaviors and to understand their significance. The principal aim of this work is to develop an intelligent rough clustering technique, which will efficiently remove the irrelevant dimensions in a highdimensional space and obtain appropriate meaningful clusters. This paper proposes a novel biclustering technique that is based on rough set theory. The proposed algorithm uses correlation coefficient as a similarity measure to simultaneously cluster both the rows and columns of a gene expression data matrix and mean squared residue to generate the initial biclusters. Furthermore, the biclusters are refined to form the lower and upper boundaries by determining the membership of the genes in the clusters using mean squared residue. The algorithm is illustrated with yeast gene expression data and the experiment proves the effectiveness of the method. The main advantage is that it overcomes the problem of selection of initial clusters and also the restriction of one object belonging to only one cluster by allowing overlapping of biclusters.
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