*2.2. PSO*

The PSO algorithm is one of the most famous swarm intelligence algorithms [47,69,70]. It was proposed in 1995, and it has the advantages of few parameters, a simple principle, and fast convergence. This algorithm has been widely used. For example, Sun et al. [71] used the agen<sup>t</sup> model to assist the PSO algorithm to solve complex optimization problems. Through the combination of exploration and exploitation, they can better solve high-dimensional problems with limited resources. Similarly, for high-dimensional problems, Tran et al. [72] changed the fixed length of feature selection in the PSO algorithm, so that the particle swarm had a shorter length. This operation reduced the search space and gave shorter particles better performance. Thus, the overall efficiency of the PSO algorithm is improved, and it is also more suitable for high-dimensional problems. For the feature selection of the PSO algorithm, they [73] also optimized other methods. They improved the algorithm by discretizing the feature selection, and proved that the single variable discretization may reduce the performance of the feature selection stage, so they proposed a new discretization method, and go<sup>t</sup> better performance. Zhang et al. [74] considered that there was no PSO algorithm that can work in noisy and noiseless environment at the same time, so they proposed the dual-environment PSO algorithm. This new algorithm is based on the top-*k* elite particles to search, which not only guaranteed the good performance in the noise environment, but also can be re-applied to the noise-free environment, so it filled a gap in the field of the PSO algorithm.
