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Hybrid Bacteria Foraging Algorithm With PSO and DE Algorithm for Optimal Cluster Head Selection in Wireless Sensor Networks

Hybrid Bacteria Foraging Algorithm With PSO and DE Algorithm for Optimal Cluster Head Selection in Wireless Sensor Networks

Pitchaimanickam Bose
Copyright: © 2022 |Volume: 16 |Issue: 1 |Pages: 27
ISSN: 1557-3958|EISSN: 1557-3966|EISBN13: 9781683180197|DOI: 10.4018/IJCINI.301206
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MLA

Bose, Pitchaimanickam. "Hybrid Bacteria Foraging Algorithm With PSO and DE Algorithm for Optimal Cluster Head Selection in Wireless Sensor Networks." IJCINI vol.16, no.1 2022: pp.1-27. http://doi.org/10.4018/IJCINI.301206

APA

Bose, P. (2022). Hybrid Bacteria Foraging Algorithm With PSO and DE Algorithm for Optimal Cluster Head Selection in Wireless Sensor Networks. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 16(1), 1-27. http://doi.org/10.4018/IJCINI.301206

Chicago

Bose, Pitchaimanickam. "Hybrid Bacteria Foraging Algorithm With PSO and DE Algorithm for Optimal Cluster Head Selection in Wireless Sensor Networks," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) 16, no.1: 1-27. http://doi.org/10.4018/IJCINI.301206

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Abstract

Network lifetime and energy constraint are the main issues for the application of wireless sensor networks. Sensor nodes spend more energy in the communication process and affect the network lifetime. Clustering is the technique for choosing the optimal cluster head from the clusters. LEACH-C is the clustering protocol in WSN. BFA is applied in the LEACH-C protocol to form the optimal clusters. This optimization obtains more steps in the tumbling process and reaches the global optimum solution very slowly. This method directly affects the network lifetime. The above limitations are overcome by introducing the hybrid approach of bacteria foraging algorithm by integrating the PSO, and DE is applied in LEACH-C algorithm for finding the optimal cluster head. The best foraging solution is utilized in the chemotactic behavior of the bacterium by using PSO and DE algorithms. The proposed methodology increases by 66% and 77% of the alive nodes when compared to FA and BFPSO.