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

Vijo M Joy


The purpose 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. To optimize the feed forward neural network training backpropagation technique and to minimize the errors, Levenberg-Marquardt algorithm is used.  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-heuristic search 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 system is improved, when hybrid optimization is used. With this method, the system achieves an optimal accuracy of 99.23%.


Power Scheduling Artificial Neural Network Backpropagation Optimization Artificial Bee Colony



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