@Article{2020.100000165,
AUTHOR = {Vijayalakshmi. K, Anandan. P},
TITLE = {Global Levy Flight of Cuckoo Search with Particle Swarm Optimization for  Effective Cluster Head Selection in Wireless Sensor Network},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {26},
YEAR = {2020},
NUMBER = {2},
PAGES = {303--311},
URL = {http://www.techscience.com/iasc/v26n2/39951},
ISSN = {2326-005X},
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.},
DOI = {10.31209/2020.100000165}
}