Open Access
ARTICLE
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,
Intelligent Automation & Soft Computing 2020, 26(2), 303-311. https://doi.org/10.31209/2020.100000165
Abstract
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.
Keywords
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.