# An Overview of Variants and Advancements of PSO Algorithm
## 4 Advances on Particle Swarm Optimization (PSO) Algorithm
### Hybridization of PSO Algorithm
To obtain an optimal solution for the dynamic facility layout problem (DFLP), [PERSON] and [PERSON] [62] proposed a hybrid PSO (HPSO) that mapped the discrete feasible space of the DFL problem into a continuous space with the help of coding procedure, and after that, the search for good quality solutions of the problem. For further improvements in their proposed algorithm, the authors combined it with simulated annealing. With the help of different experiments using their proposed algorithm, they proved that it was comparatively efficient compared to other variants. To create a balance between the global and local search abilities, [PERSON] and [PERSON] [63] proposed a hybrid PSO algorithm (HPSOM) by integrating PSO with a genetic algorithm, using the mutation process. On the basis of the numerical experiment, the authors showed that their proposed method significantly outperformed SPSO in terms of factors such as convergence speed, stability and the quality of the solution obtained. In order to stabilize the stability of the power system, [PERSON] and [PERSON] [64] hybridized the particle swarm optimization (PSO) and the bacterial foraging optimization algorithm (BFOA), and proposed a new version, namely the bacterial swarm optimization (BSO). To check the validity of the proposed version BSO, numerical experiments were conducted by the authors and the results presented the effectiveness of the proposed controller over the original PSO and BFOA algorithm. In order to optimize the learning rate of the neural network, [PERSON] and [PERSON] [65] combined the cuckoo search algorithm with the particle swarm optimization (PSO) algorithm and proposed an improved image classification algorithm for content-based image retrieval (CBIR). On the basis of the experimental results, the authors showed the classification accuracy and faster learning with this proposed neural network algorithm. [PERSON] [66] proposed a guided hybrid approach, namely a PSO-GA algorithm, in order to solve the constrained optimization problems. On the basis of a comparative study conducted on various engineering design optimization problems, the author proved the effectiveness of the proposed algorithm over other evolutionary algorithms in searching for the near optimal global solution. [PERSON] and [PERSON] [67] proposed a new hybrid variant of particle swarm optimization (PSO) and the Grey Wolf optimizer (GWO), namely the HPSOGWO, by using exploitation capabilities from PSO and exploration capabilities from GWO. The authors tested their newly derived hybrid variant on 23 classical problems and by their experimental results showed that the newly developed variant outperformed other algorithms in respect of convergence and the accuracy of the solution obtained. [PERSON] et al. [68] presented a new hybrid algorithm by combining the advantages and strengths of both the particle swarm optimization (PSO) algorithm and the firefly algorithm (FFA), to provide a balance between the exploration and exploitation capabilities. The authors compared the performance of the newly proposed algorithm with the standard particle swarm optimization (PSO) algorithm and firefly algorithm (FFA) and yielded better results by proving its efficiency in obtaining a high classification performance. To solve different nonlinear and convex optimal power flow (OPF) problems, [PERSON] et al. [69] introduced an effective and novel hybrid firefly particle swarm optimization (HFPSO) algorithm. They coded their technique, using MATLAB software, and checked its effectiveness on several test functions, and showed that the proposed algorithm had a faster rate of convergence and had more capability to handle several complex OPF problems. [PERSON] [70] explained the basic principles of the PSO algorithm, and discussed its advantages and disadvantages. He discussed some of the improved and hybrid versions of the standard particle swarm optimization (SPSO) algorithm and suggested its future research scope.