Comparative Analysis of Intelligent Solutions Searching Algorithms of Particle Swarm Optimization and Ant Colony Optimization for Artificial Neural Networks Target Dataset
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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.
Keywords
QA76 Computer software