# An Improved Particle Swarm Optimization Algorithm for Data Classification
## 2 Materials and Methods  
### Related Work  
In [39], the authors proposed a multi-mean PSO algorithm known as the MMPSO algorithm for training multi-layer feed-forward neural networks (MLFNN). MMPSO finds better solutions than PSO and MMPSO has multiple swarms to find the best solution that is better than PSO. The authors [40] introduced a new, modified PSO algorithm with two main modifications. The first, known as a self-adaptive parameter, and the second, a strategy-based method known as the SPS-PSO algorithm, optimise feed-forward neural networks by feature selection. The SPS-PSO algorithm was applied to deal with the large-scale FFNN optimisation problem and reduce the computational complexity. The authors present a new variant of PSO (NMPSO) to solve nonlinear pattern classification problems [41]. The proposed methodology structure is an ANN that offers optimal precision for a particular problem. In addition, this study introduces a new method for selecting the maximum number of neurons (MNN). The architecture simultaneously develops transfer function types and synaptic weights. The proposed method was tested to accuracy by solving synthetic pattern recognition problems. Furthermore, the artificial neural network designed using the proposed method was compared with ANN designed manually using backpropagation and learning algorithms of Levenberg-Marquardt. Experiments were performed using 10 datasets from UCI to check the performance of the MMPSO algorithm [42]. As a result of the experiment, it was shown that the proposed algorithm was executed more efficiently than other algorithms. An improved variant of the PSO algorithm called LPSONS was presented in [43] to increase the optimisation speed of the standard PSO algorithm to train ANNs. The proposed algorithm implemented the PSO velocity operator with the Mantegna-Levy distribution to improve the diversity of the population and increase accuracy. Bottom of Form The proposed LPSONS algorithm was used to optimise the feed-forward multi-layer perceptron (MLP) ANN training. In [44], the authors presented the partial opposition-based learning with PSO, or POPSO, algorithm to increase the performance of basic PSO. Partial opposition-based learning (POBL) generates the opposite swarm of the original swarm. POPSO is used to train the MLFNN for mining medical data classification problems. Compared to all other algorithms, POPSO provides a better compromise between sensitivity and specificity when classifying medical datasets. An ANN trained with the PSO algorithm has been used to distinguish dengue hemorrhagic fever (DHF) and dengue fever (DF) patients from patients recovering or not who have Parkinson's disease [45]. Finally, NNPSO was tested with a multi-layer neural network feed-forward network (MLPFFN) classifier to classify dengue fever patients from recovered or non-recovered patients. In [17], the authors presented the WELL sequence, also known as the Well Equidistributed Long-period Linear with Particle swarm optimisation (WELL-PSO) to overcome the limitation of the basic PSO algorithm. A novel quasi-random sequence initialisation scheme, the WELL sequence is used to generate the initial population. The proposed method also trains NN and offers better results than existing training algorithms (with basic PSO methods and improved variants). The experimental results show that WELL-PSO performs better on real-world classification problems than improved variant and standard method PSO. PSO algorithms were combined with the forward feedback of the neural network in the Cleveland Clinic database to reduce the 13 effective attributes to 8 factors and optimise the accuracy and cost [46]. The researchers used the four different research classification methods, and the results show that the feature selection in the neural network FFBP and PSO algorithm is more effective. A modified PSO algorithm combined with a new training algorithm was proposed by [47] for the time series problem.