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Multi-Group Gorilla Troops Optimizer with Multi-Strategies for 3D Node Localization of Wireless Sensor Networks
Sensors ( IF 3.4 ) Pub Date : 2022-06-03 , DOI: 10.3390/s22114275
Qingwei Liang , Shu-Chuan Chu , Qingyong Yang , Anhui Liang , Jeng-Shyang Pan

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).

中文翻译:

具有多策略的多组大猩猩部队优化器,用于无线传感器网络的 3D 节点定位

无线传感器网络中节点的定位问题往往是许多研究的重点。本文提出了一种基于对立的学习和并行策略人工大猩猩部队优化器 (OPGTO) 来减少定位误差。基于对立的学习可以扩展算法的探索空间,显着提高算法的全局探索能力。并行策略将种群分成多个群体进行探索,有效地增加了种群的多样性。基于这种并行策略,我们针对不同类型的优化问题设计了组间通信策略。为验证所提OPGTO算法的优化效果,在CEC2013基准函数集上进行测试,并与粒子群优化算法(PSO)进行比较,正余弦算法(SCA)、鲸鱼优化算法(WOA)和人工大猩猩部队优化器(GTO)。实验研究表明,OPGTO具有良好的优化能力,尤其是对复杂的多峰函数和组合函数。最后,我们将 OPGTO 算法应用于真实地形中无线传感器网络的 3D 定位。实验结果证明,OPGTO可以有效降低基于到达时间差(TDOA)的定位误差。
更新日期:2022-06-03
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