**4. Challenges and gaps**

Particle Swarm Optimization is a heuristic optimization method that performs well for various optimization problems. But like other swarm intelligence-based optimization technique, PSO has some disadvantages including sensitivity to parameters, high computational complexity, slow convergence. The first reason is that PSO is unable to employ the crossover operator as utilized in genetic algorithm or differential Evolution. Therefore, the distribution of suitable information be- tween candidates is not at an essential level. Another factor can be the fact that PSO is unable to handle appropriately the relationship between exploration and exploitation, in fact, local search and global search, so it often converges to a local minimum quickly. One of the solutions that can address these problems is hybridization. Numerous optimization algorithms have been utilized for ANN optimization like GA that some of them can be seen in this paper. For future work, PSO can be hybridized with some of these optimization algorithms like GA, SA, TS, DE, ABC, and ACO to develop hybrid approaches in order to achieve better exploration ability.

Another challenge is that study of PSO for optimizing NN had great achievements but there is no in-depth research on theoretical aspects. So, we think it can be interesting to conduct another study of both the run-time and convergence properties of PSO for optimizing NN. In addition, there are not many works related to PSO implemented in parallel for optimizing NN. Thus, it can be a potential path for future research. Moreover, considering other Deep Learning.

Finally, stream data poses significant challenges in this area. In a non-stationary environment, like weather forecasting and stock-price market, data comes in the stream. So, it can be a good topic to design strategies for the dynamic training of NN using PSO.
