## 2 Review of the Standard PSO and Its Variants
### 3.3 Learning strategy  
In PSO, the learning strategy plays a very important role because it is one of the key factors determining the particle search direction. In the global PSO (GPSO) [17], the best historical experience of the whole population is selected as an example of a particle learning. Similarly, a particle selects its local neighbors' best positions as its learning exemplar in the local PSO (LPSO) [30]. In OLPSO [19], the method of orthogonal experimental design (OED) is used to construct the particles' learning exemplar and the combination of the historical best experience of particles and its neighborhood's best historical guide particles to move toward the global optimal region. In the comprehensive learning PSO (CLPSO) [21], any dimension of a particle position can learn from its historical best position or other particles with better positions through a comprehensive learning strategy, while retaining the ability to learn from the global optimal experience. Similarly, in the two-swarm learning PSO (TSLPSO) [20], a novel dimensional learning (NDL) strategy is constructed by learning from the corresponding dimensions of the population best solution through each dimension of the particle's personal best experience to discover and integrate promising information, which focuses more on local search. In CCPSO-ISM [37], an information sharing mechanism (ISM), and a kind of competitive and cooperative operator (CC), are proposed to enhance the PSO performance. The ISM needs to keep the best information of each particle so that other particles can read the shared information, which also realizes full communication between the particles in the population and each other. The CC operation provides a more efficient way to utilize this shared information. In PSO-DLS [16], a learning strategy focusing on exploration is proposed in which particles can learn from a united local best position and can also obtain information from other subswarms to improve themselves. In [8, 28], an elite learning strategy guides the best knowledge of each particle to the global optimum by effectively sharing the best experience of the dominant particles when the evolution process is in a convergent state. However, in many cases, researchers have preferred to integrate multiple learning strategies to effectively guide particles toward a global optimization to meet the needs of distinct information at the different evolutionary stages.  
**(4)** Hybridization of PSO with other algorithms  
Some scholars have improved the performance of PSO by hybridizing the evolutionary mechanisms of other meta heuristic algorithms such as ACO, GA, DE, and GSA. In the genetic learning PSO (GL-PSO) [38], genetic operators are used to generate exemplars with high quality and rich diversity by performing crossover, mutation, and selection on the historical information of the particles. One of the advantages of DE is that it maintains population diversity. In [9, 39, 40], the differential evolution strategy was introduced into the particle swarm optimization algorithm, which could effectively improve the global optimization ability of a PSO. A novel hybrid PSO and GSA (HPSO-GSA) [41] uses coevolutionary technology, which updates particle positions simultaneously with PSO velocity and GSA acceleration, to seek an effective balance between exploration and exploitation. In the ant particle optimization algorithm (HAP) [42], the PSO and ACO work and generate the best solution, taken as the global best solution, which is used as an important basis for selecting the position of next generation particles and ants. In addition, various other algorithms are hybridized with PSO to improve the overall performance such as HPPSO [43] (PSO and POA), HFPSO [44] (Firefly and PSO), PSO-BOA [45], PSOGWO [46], and so on.