Hybrid Approach of Genetic Algorithm and Differential Evolution in WSN Localization

Nithya, B.; Jeyachidra, J.

doi:10.1109/icses52305.2021.9633962

Cited by 1 publication

(1 citation statement)

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“…Additionally, the initial population area of the genetic algorithm is reduced to improve convergence speed, stability, and localization accuracy. Different techniques that combined GA and Differential Evolution (DE) were proposed in [121]. GA-DE combines the strengths of GA and DE, utilizing GA's selection and crossover operators and DE's powerful mutation operator.…”

Section: A Evolutionary Algorithmsmentioning

confidence: 99%

Optimization Algorithms for Wireless Sensor Networks Node Localization: An Overview

Ahmad,

Alhasan,

Wazirali

et al. 2024

IEEE Access

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Wireless Sensor Networks (WSNs) play a critical role in numerous applications, and the accurate localization of sensor nodes is vital for their effective operation. In recent years, optimization algorithms have garnered significant attention as a means to enhance WSN node localization. This paper presents an in-depth exploration of the necessity of localization in WSN nodes and offers a comprehensive review of optimization algorithms used for this purpose. The review encompasses a diverse range of optimization techniques, including evolutionary algorithms, swarm intelligence, and metaheuristic approaches. Key factors, such as localization accuracy, scalability, computational complexity, and robustness, are systematically evaluated and compared across various optimization algorithms. Additionally, the paper sheds light on the strengths and limitations of each optimization approach and discusses their applicability in different WSN deployment scenarios. The insights provided in this review serve as a valuable resource for researchers and practitioners seeking to optimize WSN node localization, thus promoting the efficient and reliable operation of WSNs in diverse real-world applications.

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Section: A Evolutionary Algorithmsmentioning

confidence: 99%