A Survey on the Optimization of Artificial Neural Networks Using Swarm Intelligence Algorithms

Artificial Neural Networks (ANNs) are becoming increasingly useful in numerous areas as they have a myriad of applications. Prior to using ANNs, the network structure needs to be determined and the ANN needs to be trained. The network structure is usually chosen based on trial and error. The training, which consists of finding the optimal connection weights and biases of the ANN, is usually done using gradient-descent algorithms. It has been found that swarm intelligence algorithms are favorable for both determining the network structure and for the training of ANNs. This is because they are able to determine the network structure in an intelligent way, and they are better at finding the most optimal connection weights and biases during the training as opposed to conventional algorithms. Recently, a number of swarm intelligence algorithms have been employed for optimizing different types of neural networks. However, there is no comprehensive survey on the swarm intelligence algorithms used for optimizing ANNs. In this paper, we present a review of the different types of ANNs optimized using swarm intelligence algorithms, the way the ANNs are optimized, the different swarm intelligence algorithms used, and the applications of the ANNs optimized by swarm intelligence algorithms.