Open Accessartificial bee colony optimization algorithm for adaptive power scheduling in an isolated system
Author(s) -
Vijo M. Joy,
S. Krishnakumar
Publication year - 2021
Publication title -
indonesian journal of electrical engineering and computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.241
H-Index - 17
eISSN - 2502-4760
pISSN - 2502-4752
DOI - 10.11591/ijeecs.v21.i2.pp1168-1175
Subject(s) - backpropagation , artificial neural network , maxima and minima , computer science , scheduling (production processes) , mathematical optimization , computation , artificial bee colony algorithm , algorithm , convergence (economics) , electric power system , power (physics) , artificial intelligence , mathematics , mathematical analysis , physics , quantum mechanics , economics , economic growth
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-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%.