Comparative Analysis of Intelligent Solutions Searching Algorithms of Particle Swarm Optimization and Ant Colony Optimization for Artificial Neural Networks Target Dataset

The optimizations approaches of ant colony optimization (ACO) and particle swarm optimization (PSO) were targeted at improving the outcomes of artificial neural networks for finding best solution in the space. Both ACO and PSO are derived from the artificial intelligence concept that imitate the natural behaviors of animals in finding best path to foodstuff location relative and back their nest. The artificial neural networks (ANNs) are reliant on estimated research scheme in which models are generated for unspecified function in order find suitable interrelationships in input and output datasets. These are not without challenges including large time of computation, expansive hidden layer size, and poor accuracy. This paper examines the effects of pretraining dataset with ACO and PSO prior training process of ANN in order to overcome the aforementioned problems of speed and accuracy through optimization of the local and global minima. The outcomes of the study revealed that the ACO outperformed PSO in conjunction with ANN in terms of RAE, MSE, RMSE, and MAPE utilized. The error rates of ANN pretrained with ACO and PSO distinctively are 62 and 73% accordingly. Benchmarking the results against the solution optimization studies, ACO and PSO algorithms are most preferred in finding the best solution or nearest-optimal in search spaces.