**5. Conclusion**

ANN as a fertile approach to developing an intelligent information processing system has been introduced. Specifically, ANNs have been seen as a powerful tool in


*Designing Artificial Neural Network Using Particle Swarm Optimization: A Survey DOI: http://dx.doi.org/10.5772/intechopen.106139*


#### **Table 1.**

*Optimization types that researchers used.*

modern AI techniques. To utilize a prediction model based upon ANN, we face some challenges that ANN training is one of the major of them. For training ANN, conventional algorithms are used which results in researchers faced some problems. These conventional algorithms like backpropagation, are local search methods that exploit the current solution to produce a new solution. However, they lack exploration ability, hence, they often, finds local minima of an optimization problem. Unlike conventional approaches, metaheuristics like PSO are good at both exploration and exploitation and are able to solve simultaneous adaptation in each component of NN. In this paper, we present a survey of optimizing and training ANNs with using PSO that is one of the best metaheuristic algorithms for optimizing ANN. We try to review some studies conducted on optimizing ANN using PSO for different goals including comparing different methods results and solving various types of problems. In this study, all the papers are grouped into categories including the kind of PSO, year of publication, activation fitness function types, and what has been optimized. Findings in this study provide future direction for further work on optimizing ANN with using PSO (**Table 1**).

*Designing Artificial Neural Network Using Particle Swarm Optimization: A Survey DOI: http://dx.doi.org/10.5772/intechopen.106139*
