Abstract

A novel hybrid approach is developed based on the hybridization of Biogeography Based Optimization and Discrete Hopfield Neural Network. BBO algorithm is employed to tune for the optimal weights of discrete Hopfield Neural Network leading to the minimization of energy function. The proposed hybrid BBO-DHNN is implemented for 10, 20, 40 and 60 units power system under consideration. Based on the simulation results presented, it is clearly noted that the proposed HBDHNN approach results in better solutions for the unit commitment problem considered and this in turn reduces the computational burden to a significant extent. The proposed approaches are developed in MATLAB environment version 7.8.0.347 and executed in a PC with Intel core 2 Duo processor with 2.27 GHz speed and 2 GB RAM with 64 bit operating system.

Keywords:

Artificial neural network, biogeography based optimization, discrete hopfield neural network, gravitational search algorithm , unit commitment problem,

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Competing interests

The authors have no competing interests.

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