# An Overview of Variants and Advancements of PSO Algorithm
## 4 Advances on Particle Swarm Optimization (PSO) Algorithm
### Hybridization of PSO Algorithm  
When the PSO algorithm is combined with some traditional and evolutionary optimization algorithms, in order to improve the effectiveness and efficiency of the algorithm and to compensate for the weakness of each other, then that is termed as the hybridization of the PSO. One can also say that combining the advantages of the PSO with the advantages of another optimization algorithm is called the hybridization of PSO algorithm, or the blending of the PSO with another optimization algorithm. The researchers hybridized PSO with many other algorithms, including the genetic algorithm (GA), the differential evolution (DE), the Ant Colony optimization (ACO), the simulated annealing (SA), the artificial Bee colony (ABC), etc. and found very promising results. Nowadays, the PSO has become one of the researchers' choices for the purposes of hybridization. [PERSON] and [PERSON] [52] introduced the qPSO algorithm by hybridizing the PSO with a quadratic approximation operator. The authors considered the two variants of the PSO algorithm, namely the PSO with linearly decreasing inertia (PSO-W) and the PSO with constriction factor (PSO-C) for the purpose of hybridization, and proposed two new variant of PSO, namely qPSO-W and qPSO-C. The performance of the proposed variant was tested on 15 benchmark problems. The numerical results showed that the new hybrid PSO variants outperformed the basic PSO variants. For the purpose of microgrid optimization, [PERSON] et al. [53] compared their proposed Multi-PSO-SVM prediction model with the other three algorithms and proved its superiority over them. To force the PSO algorithm to jump out of the stagnation situation, [PERSON] et al. [54] hybridized particle swarm optimization (PSO) with differential evolution (DE) and proposed a novel hybrid algorithm, namely PSO-DE. On the basis of numerical experiments on constrained numerical and engineering optimization functions, the authors showed that their proposed novel approach resolved the convergence issues of basic PSO and also improved the quality of the solution obtained. To overcome the various problems related to the PSO algorithm, [PERSON] et al. [55] proposed an HPSO algorithm, and with experimental results proved the higher rate of stability, clustering efficiency and the global search ability of their algorithm over others. By using python tools, [PERSON] et al. [56] developed a PSO-LSTM-based model to investigate the speed of road traffic in Kunming City, China. [PERSON] et al. [57] introduced a new hybrid version of the particle swarm optimization (PSO) algorithm, namely the hybrid particle swarm optimization (HPSO) by combining two different approaches of PSO i.e., standard particle swarm optimization (SPSO) and mean particle swarm optimization (MPSO). By conducting numerical experiments on several benchmark problems, the authors concluded that that their proposed algorithm worked efficiently in comparison to SPSO and MPSO, in terms of speed and quality of solution. For the purpose of grid scheduling and resource management, [PERSON] and [PERSON] [58] proposed a PSO-based Ba-PSO scheduling algorithm. [PERSON] et al. [59] introduced a PSO-SVM regression model and proved its higher accuracy by proving that the average relative errors of the regression results of the specific supportedroadway i.e., the wood-supported roadway, the I-steel-supported roadway and the bolt-net-supported roadway were all \(<\)5%. [PERSON] [60] proposed an improved PSO-BP combined forecasting model. This newly combined forecasting model is used in the college student entrepreneurship prediction experiment. [PERSON] et al. [61] presented a hybrid version of PSO, namely Particle Filter-Particle Swarm Optimization (PF-PSO) algorithm and proved its application to the fuzzy-controlled servo system.