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Global Levy Flight of Cuckoo Search with Particle Swarm Optimization for Effective Cluster Head Selection in Wireless Sensor Network

Vijayalakshmi. K1,*, Anandan. P2

1 Department of Electronics and communication Engineering, SKP Engineering College, Tiruvannamalai, India.
2 Department of Electronics and communication Engineering, C. Abdul Hakeem College of Engineering & Technology, Melvisharam, India.

* Corresponding Author: Vijayalakshmi K, email

Intelligent Automation & Soft Computing 2020, 26(2), 303-311.


The advent of sensors that are light in weight, small-sized, low power and are enabled by wireless network has led to growth of Wireless Sensor Networks (WSNs) in multiple areas of applications. The key problems faced in WSNs are decreased network lifetime and time delay in transmission of data. Several key issues in the WSN design can be addressed using the Multi-Objective Optimization (MOO) Algorithms. The selection of the Cluster Head is a NP Hard optimization problem in nature. The CH selection is also challenging as the sensor nodes are organized in clusters. Through partitioning of network, the consumption of energy was improved and through evolutionary protocols for the selection of optimized CHs, the position information and residual energy are considered by the WSNs. There is a need for MOO vision for tackling this issue. Because of its ease of implementation, highly efficient solution, quick convergence and the capability of avoiding the local optima, for such NP hard problem the Particle Swarm Optimization (PSO) is the significant effective algorithms that have been inspired by nature. Another algorithm is the Cuckoo Search (CS) algorithm. The Global Levy Flight of CS with PSO is proposed to get improved network performance incorporating balanced energy dissipation and results in the formation of optimum number of clusters and minimal energy consumption.


Cite This Article

V. K and A. P, "Global levy flight of cuckoo search with particle swarm optimization for effective cluster head selection in wireless sensor network," Intelligent Automation & Soft Computing, vol. 26, no.2, pp. 303–311, 2020.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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