## 2 Related Work
In this work, they introduced different selection techniques to select inertia weight. They named it as PSO-BP. In the end, the BP algorithm was used to search around the global optimum. They compared the result with adaptive particle swarm optimization (APSOA) and back propagation algorithm and found better results. This work is done by [40]. In this work, they used PSO to optimize weight and architecture of MLP feed-forward neural network. They have used PSO algorithms twice: the inner one and the outer one. The inner one was used to optimize the weight, and the outer one was used for architecture optimization. In the proposed technique, the outer PSO searches the hidden nodes of each hidden layers of the MLP networks. To improve the generalization, they used weight decay heuristics in the inner PSO. The performance of the proposed technique was applied to the famous benchmark classification problem in the medical field. The result shows better generalization for all data sets. PSO-ANN is proposed by [41]. In this work, the authors showed a better performance of ANN by using PSO to adjust the weights. In the proposed technique, both the velocity and position of each particle was initialized randomly in a specific dimension to train the PSO-ANN based on particle position and calculated its fitness. They saved the current position and fitness as history. The individual best among all the particle was considered to be the best. The new set of position generated by this method was used for learning error generation. Every particle's position and velocity was updated according to its personal best and global best. This helped the biases and weights trapping in a local minimum. The performance of PSO-ANN is compared with many backpropagation algorithms such as Levenberg-Marquardt and gradient descent algorithms resulting in better performance. A new variant of PSO combining with stochastic gradient decent is proposed by [42] and named the PSO-SGD algorithm for training convolution neural network [42]. The proposed technique worked into two phases. PSO was used to train and initialize the CNN parameters in the first phase. When it showed, slow progress of PSO for a few iteration SGD was used in the second phase. In addition, they used PSO combining with genetic algorithm (GA), which helped the particle for simulation and overcame the slowness of SGD. They applied the new algorithm on a different benchmark data set and performed well for three different data sets. The proposed technique avoided the occurrence of local optimum and premature saturation, as it was in the known problem by using any single algorithm. The authors in [43] examined the impact of initiating the initial population by excluding traditional techniques such as random numbers. The authors applied the nonlinear simplex method for generating the initial population of DE, where the proposed algorithm was termed NSD. The working of the proposed algorithm is measured with twenty benchmark functions and compared with standard DE and opposition-based DE (ODE) algorithm. Numerical results illustrate that the proposed technique enhances the convergence rate. During the image segmentation process, to solve the issue of thresholding, an enhanced variant of standard DE algorithm with a local search (termed as LED) and low-discrepancy sequences is introduced [44]. Experimental results conclude that the performance of the introduced algorithm is superior for finding the optimum threshold. For the steelmaking continuous (SCC) problem, in [45], the authors presented a novel enhanced technique of DE based on the two-step procedure for producing an initial population, as well as, a novel mutation approach. Furthermore, an incremental methodology for generating the initial population is also incorporated in DE to handle dynamic events. Computational experiments conducted with the presented approach show the effectiveness of the presented approach than others. In the time-series prediction, the backpropagation neural network (BPNN) gets stuck into local optima.