Application of Computational Intelligence Algorithms in Radio Propagation: A Systematic Review and Metadata Analysis

Table 6

Summary of particle swarm optimization computational process.

Authors	Aims	Methods
He et al. [57]	To design an RBF-based neural network adaptive particle swarm optimization algorithm	PSO + neural network
Tahat and Taha [58]	To propose the application of a statistical tuning technique based on particle swarm optimization (PSO) to adjust the COST 231 Walfisch-Ikegami for path loss prediction	PSO
Olukunle et al. [59]	To propose the development of an optimized model for urban outdoor coverage at 2300 MHz frequency for LTE network systems in Port Harcourt urban terrain roads (Rumuokoro, Eneka and Ikwerre)	PSO
Chiu et al. [60]	To evaluate the performance of a particle swarm optimization (PSO) model and a genetic algorithm (GA) for path loss estimation in an urban area	PSO + GA
Al Salameh and Al-Zu’bi [61]	To propose the path loss prediction for mobile phone stations in an outdoor environment in the 900 MHz and 1800 MHz bands	PSO
Omae et al. [16]	To conduct a study and report the path loss prediction of a Wi-Fi signal propagation along a passageway using particle swarm optimization (PSO) trained adaptive neural fuzzy inference system (ANFIS), ANFIS, and PSO trained with a random input	PSO + ANFIS
Garah et al. [62]	To propose the application of particle swarm optimization for the tuning of the COST 231 model parameters for the improvement of its accuracy for path loss prediction	PSO
Xiang and Wang [63]	To report the application of PSO-BP-Kriging for 5G signal coverage detection	Enhanced particle swarm optimization algorithm