Artificial Bee Colony (ABC) Optimization Algorithm for Training Feed-Forward Neural Networks

Karaboğa, Derviş; Akay, Bahriye; Öztürk, Celal

doi:10.1007/978-3-540-73729-2_30

Cited by 366 publications

(155 citation statements)

References 11 publications

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“…The application of the ABC algorithm is relatively simple [26] and it has the advantage of not requiring a lot of parameters to be tuned [28]. All processes related to proposed ANN-ABC model are illustrated in (Figure.1).…”

Section: Proposed Ann-abc Modelmentioning

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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Section: Proposed Ann-abc Modelmentioning

confidence: 99%

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

Kayabaşı

2018

ijisae

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“…1. Further studies in [22][23][24][25][26][27][28] has proved that the ABC algorithm has a better performance in results and solutions compared with other popular population-based and heuristic optimization algorithms.…”

Section: Abc Algorithm Coupling With Ppf Modelmentioning

confidence: 99%

A Congestion Management Approach Using Probabilistic Power Flow Considering Direct Electricity Purchase

Wang¹,

Jiang²

2015

Journal of Electrical Engineering and Technology

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-In a deregulated electricity market, congestion of the transmission lines is a major problem the independent system operator (ISO) would face. Rescheduling of generators is one of the most practiced techniques to alleviate the congestion. However, not all generators in the system operate deterministically and independently, especially wind power generators (WTGs). Therefore, a novel optimal rescheduling model for congestion management that accounts for the uncertain and correlated power sources and loads is proposed. A probabilistic power flow (PPF) model based on 2m+1 point estimate method (PEM) is used to simulate the performance of uncertain and correlated input random variables. In addition, the impact of direct electricity purchase contracts on the congestion management has also been studied. This paper uses artificial bee colony (ABC) algorithm to solve the complex optimization problem. The proposed algorithm is tested on modified IEEE 30-bus system and IEEE 57-bus system to demonstrate the impacts of the uncertainties and correlations of the input random variables and the direct electricity purchase contracts on the congestion management. Both pool and nodal pricing model are also discussed.

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“…A comprehensive survey regarding evolutionary neural networks is available in (Yao, 1999). Recently, swarm intelligence techniques, including PSO (Mendes et al, 2002;Carvalho and Ludermir, 2006), ABC (Karaboga et al, 2007;Karaboga and Ozturk, 2009) and ACO (Socha and Blum, 2007), were also used to train neural networks. A review for other metaheuristics used for training neural networks is available in (Alba and Marti, 2006).…”

Section: Training Artificial Neural Networkmentioning

confidence: 99%

Training neural networks with ant colony optimization algorithms for pattern classification

Mavrovouniotis

Yang

2014

Soft Comput

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Feed-forward neural networks are commonly used for pattern classification. The classification accuracy of feed-forward neural networks depends on the configuration selected and the training process. Once the architecture of the network is decided, training algorithms, usually gradient descent techniques, are used to determine the connection weights of the feed-forward neural network. However, gradient descent techniques often get trapped in local optima of the search landscape. To address this issue, an ant colony optimization (ACO) algorithm is applied to train feed-forward neural networks for pattern classification in this paper. In addition, the ACO training algorithm is hybridized with gradient descent training. Both standalone and hybrid ACO training algorithms are evaluated on several benchmark pattern classification problems, and compared with other swarm intelligence, evolutionary and traditional training algorithms. The experimental results show the efficiency of the proposed ACO training algorithms for feed-forward neural networks for pattern classification.

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Artificial Bee Colony (ABC) Optimization Algorithm for Training Feed-Forward Neural Networks

Cited by 366 publications

References 11 publications

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

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

A Congestion Management Approach Using Probabilistic Power Flow Considering Direct Electricity Purchase

Training neural networks with ant colony optimization algorithms for pattern classification

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