Using artificial bee colony algorithm for MLP training on earthquake time series data prediction
Nowadays, computer scientists have shown the interest in the study of social insect’s behaviour in neural networks area forsolving different combinatorial and statistical problems. Chief among these is the Artificial Bee Colony (ABC) algorithm. This paper investigates the use of ABC algorithm that s...
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| Main Authors: | , , |
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| Format: | Article |
| Published: |
2011
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/2984/ |
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| Summary: | Nowadays, computer scientists have shown the interest in the study of social insect’s behaviour in neural networks area forsolving 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 thecrucial problems with the backpropagation (BP) algorithm is that it can sometimes yield the networks with suboptimal weights becauseof the presence of many local optima in the solution space. To overcome ABC algorithm used in this work to train MLP learning thecomplex behaviour of earthquake time series data trained by BP, the performance of MLP-ABC is benchmarked against MLP trainingwith the standard BP. The experimental result shows that MLP-ABC performance is better than MLP-BP for time series data. |
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