*2.4. Hybridization*

Researchers also modified the PSO algorithm by combining it with other optimizers for the purpose of enhancing the performance and expanding the search ability of the particles during the evolution process. According to recent research work, when PSO integrates with other evolutionary operators such as crossover, selection, and mutation, the efficiency of the PSO improves and the PSO is strengthened in terms of robustness, stability, and convergence rate. In [32], the genetic algorithm (GA) is used to amend the decision vectors using genetic operators, while the PSO is used to boost vector position. In [33], the PSO algorithm is paired with the sine cosine algorithm (SCA) and levy flight distribution. According to the SCA algorithm, the updating solution is based on the sine and cosine functions, while levy flight is a random walk that uses the levy distribution to produce search steps and then uses big spikes to search the exploration space more effectively. A new hybrid algorithm is proposed that combines the exploitation capabilities of the PSO with the integration of the exploration capabilities of the grey wolf optimizer (GWO). On the basis of the idea, it combines two methods by substituting a particle from the PSO with a low probability for a partially better particle from the GWO [34]. The hybridization method of PSO and differential evolution (DE) has been reported in [35]. The main idea of the proposal is to control diversity and keep a good balance between the local and global searches of the candidates.

Indeed, PSO has been widely used in large areas of research such as in the application of face recognition systems [36], artificial neural network [37], Internet of Things [38], reliability engineering [39], power-system [40], indoor navigation [41], control-systems [42], EEG signals [43], deep-learning [44], wireless sensor networks [45], cloud computing [46], energy grid [47], Image segmentation [48], and electromagnetics [49,50].
