Modified artificial bee colony optimization algorithm for adaptive power scheduling in an isolated system

Abstract

The objective of this work is to solve the power scheduling problems for efficient energy management by assigning the optimal values. Artificial neural networks are used widely in the field of energy management and load scheduling. The  backpropagation technique is used for the feed-forward neural network training and the Levenberg-Marquardt algorithm is used to minimize the errors. The slow speed of convergence and getting stuck in local minima are some negatives of   backpropagation in complex computation. To overcome these drawbacks an innovative meta-heuristicsearch algorithm called modified artificial bee colony optimization algorithm is used. A hybrid neural network is introduced in this work.  The simulation result shows that the efficiency of the systemis improved when hybrid optimization is used. With this method, the system achieves an optimalaccuracy of 99.23%