Multi-Group Gorilla Troops Optimizer with Multi-Strategies for 3D Node Localization of Wireless Sensor Networks

The localization problem of nodes in wireless sensor networks is often the focus of many researches. This paper proposes an opposition-based learning and parallel strategies Artificial Gorilla Troop Optimizer (OPGTO) for reducing the localization error. Opposition-based learning can expand the explo...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2022-06, Vol.22 (11), p.4275
Main Authors: Liang, Qingwei, Chu, Shu-Chuan, Yang, Qingyong, Liang, Anhui, Pan, Jeng-Shyang
Format: Article
Language:English
Subjects:
3D node localization
Algorithms
Animal behavior
artificial gorilla troops optimizer
Combinatorial analysis
Communication
Exploitation
Fire prevention
Intelligence
Localization
Mathematical optimization
Normal distribution
opposition-based learning
Optimization algorithms
parallel communication strategy
Sensors
Wireless networks
Wireless sensor networks
Wireless Technology
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Summary:The localization problem of nodes in wireless sensor networks is often the focus of many researches. This paper proposes an opposition-based learning and parallel strategies Artificial Gorilla Troop Optimizer (OPGTO) for reducing the localization error. Opposition-based learning can expand the exploration space of the algorithm and significantly improve the global exploration ability of the algorithm. The parallel strategy divides the population into multiple groups for exploration, which effectively increases the diversity of the population. Based on this parallel strategy, we design communication strategies between groups for different types of optimization problems. To verify the optimized effect of the proposed OPGTO algorithm, it is tested on the CEC2013 benchmark function set and compared with Particle Swarm Optimization (PSO), Sine Cosine Algorithm (SCA), Whale Optimization Algorithm (WOA) and Artificial Gorilla Troops Optimizer (GTO). Experimental studies show that OPGTO has good optimization ability, especially on complex multimodal functions and combinatorial functions. Finally, we apply OPGTO algorithm to 3D localization of wireless sensor networks in the real terrain. Experimental results proved that OPGTO can effectively reduce the localization error based on Time Difference of Arrival (TDOA).
ISSN:1424-8220
1424-8220
DOI:10.3390/s22114275