# An Overview of Variants and Advancements of PSO Algorithm
## 4 Advances on Particle Swarm Optimization (PSO) Algorithm
### Modifications of Original PSO  
There are modifications of the PSO algorithm, including fuzzy PSO, bare-bones PSO, etc. [PERSON] and [PERSON] [22] made a modification in the original particle swarm algorithm by introducing the concept of Gaussian probability distribution. In their proposed algorithm, they only needed the parameter 'the number of particles' to be specified before actually using the algorithm. The experimental results of the Gaussian particle swarm algorithm showed that the Gaussian swarm outperformed the standard PSO. [PERSON] and [PERSON] [23] introduced a new version of the PSO, called concurrent PSO (CONPSO), to improve the convergence performance of the original PSO. By testing this new version of the PSO on six classical functions, the authors proved that this new version of the PSO, i.e., the CONPSO, outperformed many other approaches of PSO. [PERSON] and [PERSON] [24] networked the original particle warm optimization (PSO) technique and introduced a binary particle swarm. According to the authors, this new version of the PSO algorithm could optimize any function, continuous or discrete. They discussed this algorithm with various examples and applications. To make the basic particle swarm algorithm more understandable, [PERSON] [25] eliminated the velocity formula. The author compared several variations and discovered its similarity with other stochastic population-based problem-solving methods, and suggested new avenues of investigation.  
[PERSON] et al. [26] modified the original PSO algorithm by the concept of fully informed particles in the original PSO, as they felt that each of the particles in the swarm need not be affected only by the best performer among his neighbors. With the help of numerical experiments, the authors revealed that this new modified version of PSO outperformed the original PSO. [PERSON] et al. [27] proposed to resolve the classification problems by making some modifications in the original PSO. So, a binary version of the PSO algorithm was compared with some machine-learning techniques, and the authors showed the promising results. [PERSON] and [PERSON] [28] made use of a fuzzy system to adopt the inertia weight of the particle swarm optimization (PSO) algorithm and introduced a new variant of PSO, called fuzzy adaptive PSO. Experiments on three test functions concluded that this fuzzy adaptive PSO was a promising optimization technique.  
To detect facial emotion, [PERSON] et al. [29] presented a novel approach called guided particle s optimization (GPSO) by modifying the original particle swarm optimization (PSO) algorithm. The authors proved that their emotion detection algorithm gave promising results in terms of accuracy in the detection of emotions. In order to obtain an optimum solution, [PERSON] et al. [30] derived a new PSO algorithm, namely the self-regulating PSO (SRPSO) algorithm, by incorporating the best of the human-learning strategies. The authors proposed the learning strategies, on the one hand using a self-regulatory inertia weight for better exploration, and on the other hand using self-perception of the global search space for better exploitation. The statistical analyses indicated the superiority of the SRPSO over other metaheuristics. Presently, many researchers are also working on the further modification of the original PSO algorithm.