Open AccessAn Elite Adaptive Niche Evolutionary Algorithm for Duty Clustering Problem in SoWSN
Author(s) -
Jinbin Bai,
Min Tian,
Jiangquan Li
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1769/1/012067
Subject(s) - duty cycle , computer science , cluster analysis , mathematical optimization , genetic algorithm , selection (genetic algorithm) , wireless sensor network , algorithm , distributed computing , engineering , artificial intelligence , mathematics , machine learning , computer network , voltage , electrical engineering
The recent success of emerging low power wireless sensor networks technology has encouraged researchers to create novel duty cycle design algorithm in this area. Since “sensors are constrained in sensing capabilities”, duty cycle design plays a crucial role in maximizing the point coverage rate, while most researches for duty cycle design are related to a duty cycle design algorithm. But unfortunately, the ideal duty cycle design requires an exhaustive search over all combinations of the allowed combinations. In this letter, we present a new elite adaptive niche evolutionary algorithm (EANEA) for duty cycle design problem in Service-oriented wireless sensor networks (SoWSN). In order to extend the network life cycle, we designed an objective function for SoWSN. We also give an EANEA which, depending on a powerful niche operator, blends the merits of both elite selection and adaptive adjusting for the channel assignment problem. Simulation results show that the shown algorithm can achieve a higher point coverage rate over genetic quantum algorithm (QGA) and Shuffled Frog-Leaping Algorithm (SFLA). Moreover, the optimization employs an elite selection to initialize the parameters and avoid local optima proficiently.