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

Using Artificial Bee Colony Algorithm for MLP Training on Earthquake Time Series Data Prediction