  Nowadays, computer scientists have shown the interest in the study of social
insect's behaviour in neural networks area for solving different combinatorial
and statistical problems. Chief among these is the Artificial Bee Colony (ABC)
algorithm. This paper investigates the use of ABC algorithm that simulates the
intelligent foraging behaviour of a honey bee swarm. Multilayer Perceptron
(MLP) trained with the standard back propagation algorithm normally utilises
computationally intensive training algorithms. One of the crucial problems with
the backpropagation (BP) algorithm is that it can sometimes yield the networks
with suboptimal weights because of the presence of many local optima in the
solution space. To overcome ABC algorithm used in this work to train MLP
learning the complex behaviour of earthquake time series data trained by BP,
the performance of MLP-ABC is benchmarked against MLP training with the
standard BP. The experimental result shows that MLP-ABC performance is better
than MLP-BP for time series data.
