Artificial neural network synthesis by means of artificial bee colony (ABC) algorithm

2011 ◽  
Author(s):  
Beatriz A. Garro ◽  
Humberto Sossa ◽  
Roberto A. Vazquez
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Artificial Bee Colony ◽  
Network Synthesis ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Artificial Neural

Enrichment of Distribution System Stability Through Artificial Bee Colony Algorithm and Artificial Neural Network

2022 ◽  
pp. 728-748
Author(s):  
Gummadi Srinivasa Rao ◽  
Y. P. Obulesh ◽  
B. Venkateswara Rao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power Loss ◽  
Artificial Bee Colony ◽  
Reactive Power ◽  
Load Flow ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this chapter, an amalgamation of artificial bee colony (ABC) algorithm and artificial neural network (ANN) approach is recommended for optimizing the location and capacity of distribution generations (DGs) in distribution network. The best doable place in the network has been approximated using ABC algorithm by means of the voltage deviation, power loss, and real power deviation of load buses and the DG capacity is approximated by using ANN. In this, single DG and two DGs have been considered for calculation of doable place in the network and capacity of the DGs to progress the voltage stability and reduce the power loss of the system. The power flow of the system is analyzed using iterative method (The Newton-Raphson load flow study) from which the bus voltages, active power, reactive power, power loss, and voltage deviations of the system have been achieved. The proposed method is tested in MATLAB, and the results are compared with particle swarm optimization (PSO) algorithm, ANN, and hybrid PSO and ANN methods for effectiveness of the proposed system.

Enrichment of Distribution System Stability Through Artificial Bee Colony Algorithm and Artificial Neural Network

2019 ◽  
pp. 35-55
Author(s):  
Gummadi Srinivasa Rao ◽  
Y. P. Obulesh ◽  
B. Venkateswara Rao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power Loss ◽  
Artificial Bee Colony ◽  
Reactive Power ◽  
Load Flow ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this chapter, an amalgamation of artificial bee colony (ABC) algorithm and artificial neural network (ANN) approach is recommended for optimizing the location and capacity of distribution generations (DGs) in distribution network. The best doable place in the network has been approximated using ABC algorithm by means of the voltage deviation, power loss, and real power deviation of load buses and the DG capacity is approximated by using ANN. In this, single DG and two DGs have been considered for calculation of doable place in the network and capacity of the DGs to progress the voltage stability and reduce the power loss of the system. The power flow of the system is analyzed using iterative method (The Newton-Raphson load flow study) from which the bus voltages, active power, reactive power, power loss, and voltage deviations of the system have been achieved. The proposed method is tested in MATLAB, and the results are compared with particle swarm optimization (PSO) algorithm, ANN, and hybrid PSO and ANN methods for effectiveness of the proposed system.

scholarly journals PENERAPAN ARTIFICIAL NEURAL NETWORK DENGAN OPTIMASI MODIFIED ARTIFICIAL BEE COLONY UNTUK MERAMALKAN HARGA BITCOIN TERHADAP RUPIAH

2020 ◽  
Vol 9 (2) ◽  
pp. 135-142
Author(s):  
Di Mokhammad Hakim Ilmawan ◽  
Budi Warsito ◽  
Sugito Sugito
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Artificial Bee Colony ◽  
Time Series Data ◽  
Learning Algorithm ◽  
Series Data ◽  
Future Period ◽  
Bee Colony ◽  
Artificial Neural ◽  
Digital Assets

Bitcoin is one of digital assets that can be used to make a profit. One of the ways to use Bitcoin profitly is to trade Bitcoin. At trade activities, decisions making whether to buy or not are very crucial. If we can predict the price of Bitcoin in the future period, we can make a decisions whether to buy Bitcoin or not. Artificial Neural Network can be used to predict Bitcoin price data which is time series data. There are many learning algorithm in Artificial Neural Network, Modified Artificial Bee Colony is one of optimization algorithm that used to solve the optimal weight of Artificial Neural Network. In this study, the Bitcoin exchage rate against Rupiah starting September 1, 2017 to January 4, 2019 are used. Based on the training results obtained that MAPE value is 3,12% and the testing results obtained that MAPE value is 2,02%. This represent that the prediction results from Artificial Neural Network optimized by Modified Artificial Bee Colony algorithm are quite accurate because of small MAPE value.

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