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

TL;DR

This paper explores using the Artificial Bee Colony algorithm to train Multilayer Perceptrons for earthquake time series prediction, showing improved performance over traditional backpropagation methods.

Contribution

It introduces a novel application of the ABC algorithm for training MLPs on earthquake data, addressing local optima issues in backpropagation.

Findings

MLP-ABC outperforms MLP-BP in accuracy

ABC reduces training time compared to BP

Improved robustness in earthquake data prediction

Abstract

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.