Hybrid Guided Artificial Bee Colony Algorithm for Earthquake Time Series Data Prediction

Shah, Habib; Ghazali, Rozaida; Herawan, Tutut; Khan, Nawsher; Khan, M. Sadiq Ali

doi:10.1007/978-3-319-10987-9_19

Cited by 17 publications

(16 citation statements)

References 42 publications

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“…Similar success with ABC trained neural networks 4 has also been claimed by numerous other authors [16,17,18,22,23], and further improved results have been obtained with hybrid learning algorithms involving the ABC combined with more traditional neural network training algorithms [9,19,21]. The key question to be addressed in this paper is: how can these good ABC results be reconciled with the earlier negative results that CantuPaz and Kamath obtained for the closely related population-based EAs [4]?…”

Section: Neural Network Training Using the Abcsupporting

confidence: 76%

“…This is clearly not preventing the ABC algorithm from finding good solutions, but, together with the finding that the scout bees are not making any useful contribution, it does mean that the ABC is actually performing little more than stochastic hill climbing, which one would expect to end up with similar results to an informed hill climbing algorithm like BP, albeit more slowly. It also means that previous claims that the ABC can avoid becoming stuck in local optima better than BP [15,16,17,22] could well prove unfounded too.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

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Artificial Bee Colony Training of Neural Networks

Bullinaria

Alyahya

2014

Studies in Computational Intelligence

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Abstract:The Artificial Bee Colony (ABC) is a swarm intelligence algorithm for optimization that has previously been applied to the training of neural networks. This paper examines more carefully the performance of the ABC algorithm for optimizing the connection weights of feed-forward neural networks for classification tasks, and presents a more rigorous comparison with the traditional Back-Propagation (BP) training algorithm. The empirical results for benchmark problems demonstrate that using the standard "stopping early" approach with optimized learning parameters leads to improved BP performance over the previous comparative study, and that a simple variation of the ABC approach provides improved ABC performance too. With both improvements applied, the ABC approach does perform very well on small problems, but the generalization performances achieved are only significantly better than standard BP on one out of six datasets, and the training times increase rapidly as the size of the problem grows. If different, evolutionary optimized, BP learning rates are allowed for the two layers of the neural network, BP is significantly better than the ABC on two of the six datasets, and not significantly different on the other four.

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Section: Neural Network Training Using the Abcsupporting

confidence: 76%

Section: Conclusion and Discussionmentioning

confidence: 99%

Artificial Bee Colony Training of Neural Networks

Bullinaria

Alyahya

2014

Studies in Computational Intelligence

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“…The domain of stock forecasting attracted researchers who hybridized ANNs with the ABC algorithm [150] and the fish algorithm [156]. A related application of ABC to earthquake time-series prediction is due to Shah et al [155].…”

Section: Hybrid Ann+simentioning

confidence: 99%

Adaptation and Hybridization in Nature-Inspired Algorithms

Fister

Strnad

Yang³

2015

Adaptation, Learning, and Optimization

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Abstract. The aim of this chapter is to familiarize readers with the basics of adaptation and hybridization in nature-inspired algorithms as necessary for understanding the main contents of this book. Adaptation is a metaphor for flexible autonomous systems that respond to external changing factors (mostly environmental) by adapting their wellestablished behavior. Adaptation emerges in practically all areas of human activities as well. Such adaptation mechanisms can be used as a general problem-solving approach, though it may suffer from a lack of problem-specific knowledge. To solve specific problems with additional improvements of possible performance, hybridization can be used in order to incorporate a problem-specific knowledge from a problem domain. In order to discuss relevant issues as general as possible, the classification of problems is identified at first. Additionally, we focus on the biological foundations of adaptation that constitute the basis for the formulation of nature-inspired algorithms. This book highlights three types of inspirations from nature: the human brain, Darwinian natural selection, and the behavior of social living insects (e.g., ants, bees, etc.) and animals (e.g., swarm of birds, shoals of fish, etc.), which influence the development of artificial neural networks. evolutionary algorithms, and swarm intelligence, respectively. The mentioned algorithms that can be placed under the umbrella of computational intelligence are described from the viewpoint of adaptation and hybridization so as to show that these mechanisms are simple to develop and yet very efficient. Finally, a brief review of recent developed applications is presented.

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“…Swarm intelligence algorithms are used to carry out some complications in the construction of the ANNs. Swarm intelligence algorithms were used to adjust the parameters of neural networks in the literature [20][21][22][23][24]. Artificial bee colony (ABC) algorithm which is one of the swarm intelligence algorithms was proposed by (Karaboga;2005) and it was inspired by collective behaviours of bees gathering honey.…”

Section: Introductionmentioning

confidence: 99%

An Application of ANN Trained by ABC Algorithm for Classification of Wheat Grains

Kayabaşı

2018

ijisae

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Artificial Neural Networks (ANNs) have emerged as an important tool for classification problem. This paper presents an application of ANN model trained by artificial bee colony (ABC) optimization algorithm for classification the wheat grains into bread and durum. ABC algorithm is used to optimize the weights and biases of three-layer multilayer perceptron (MLP) based ANN. The classification is carried out through data of wheat grains (#200) acquired using image-processing techniques (IPTs). The data set includes five grain's geometric parameters: length, width, area, perimeter and fullness. The ANN-ABC model input with the geometric parameters are trained through 170 wheat grain data and their accuracies are tested via 30 data. The ANN-ABC model numerically calculate the outputs with mean absolute error (MAE) of 0.0034 and classify the grains with accuracy of 100% for the testing process. The results of ANN-ABC model are compared with other ANN models trained by 4 different learning algorithms. These results point out that the ANN trained by ABC optimization algorithm can be successfully applied to classification of wheat grains.

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Hybrid Guided Artificial Bee Colony Algorithm for Earthquake Time Series Data Prediction

Cited by 17 publications

References 42 publications

Artificial Bee Colony Training of Neural Networks

Artificial Bee Colony Training of Neural Networks

Adaptation and Hybridization in Nature-Inspired Algorithms

An Application of ANN Trained by ABC Algorithm for Classification of Wheat Grains

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