REFERENCES
- O. Alsac, B. Stott, Optimal load flow with steady-state security, IEEE
Trans Power Apparatus Syst 93 (1974), 745-751.
- J. Radosavljević, D. Klimenta, M. Jevtić, N. Arsić, Optimal power flow
using a hybrid optimization algorithm of particle swarm optimization
and gravitational search algorithm, Electric Power Components and
Systems 43 (2015), 1958-1970.
- J. Radosavljević, Metaheuristic Optimization in Power Engineering, The
Institution of Engineering and Technology (IET), London, 2018.
- G. T. Tziasiou, G. A. Orfanos, P. S. Georgilakis and N. D.
Hatziargyriou, Transmission pricing software for power engineering
education, Computer Applications in Engineering Education, Vol. 22
,Iss. 3, 2014, pp. 410-428.
- S. Janković, Heuristic approach to solving problem of location and
relocation of ambulance vehicles in base stations, Master thesis,
University of Belgrade, Faculty of Mathematics, 2015.
- R. C. Eberhart and J. Kennedy, A new optimizer using particle swarm
theory. In: Micro machine and human science, Proceedings of the
6th international symposium, Vol. 1, 1995, pp.39-43.
- S. Mirjalili and S. Z. M. Hashim, A new hybrid PSOGSA algorithm for
function optimization, International Conference on Computer and
Information Application (ICCIA 2010), pp. 374–377, Tianjin, China,
3–5 December 2010.
- E. Rashedi, H. Nezamadi-pour and S. Saryazdi, GSA: A gravitational
search algorithm, Information Sciences, Vol. 179, 2009, pp. 2232-2248.
- D. Karaboga and B. Basturk, A powerful and efficient algorithm for
numerical function optimization: artificial bee colony (ABC)
algorithm, J Global Optim, Vol. 39, 2007, pp. 459-471.
- I. Ilhan, Mobile device based test tool for optimization algorithms,
Computer Applications in Engineering Education, Vol. 24, Iss. 5, 2016,
pp: 744–754.
- M. Rezaei Adaryani, A. Karami, Artificial bee colony algorithm for
solving muli-objective optimal power flow problem, Electr. Power
Energy Syst., Vol. 53, 2013, pp. 219-230.
- Z. Bayraktar, M. Komurcu, and D. H. Werner, Wind driven optimization
(WDO): A novel nature-inspired optimization algorithm and its
application to electromagnetics, Proceedings of the 2010 IEEE
International Symposium on Antennas and Propagation and CNC/USNC/URSI
Radio Science Meeting, Toronto, Ontario, Canada, July 11-17, 2010.
- Z. Bayraktar, M. Komurcu, Z. H. Jiang, D. H. Werner and P. L. Werner,
Stub-loaded inverted F-antenna synthesis via wind driven optimization,
in Proc. IEEE Int. Symp. Antennas Propag., USNC/URSI Nat. Radio Sci.
Meet., Spokane, WA, Jul. 3–8, 2011, pp. 2920–2923.
- Z. Bayraktar, M. Komurcu, J. A. Bosard and D. H. Werner, The wind
driven optimization technique and its application in electromagnetic,
IEEE Transactions on Antennas and Propagation, Vol. 61, No. 5, 2013,
pp. 2745-2757.
- X. S. Yang, Nature-inspired metaheuristic algorithms, UK: Luniver
Press; 2008.
- P. Balachennaiah, M. Suryakalavathi and P. Nagendra, Firefly algorithm
based solution to minimize the real power loss in a power system, Ain
Shams Eng J, 2015, http://dx.doi.org/10.1016/j.asej.2015.10.005
- S. Mirjalili, S.M. Mirjalili and A. Lewis, Grey wolf optimizer,
Advances in Engineering Software, Vol. 69, 2014, pp. 46-61.
- X.- S. Yang and S. Deb, Cuckoo search via Levy flights, Proc. of World
Congress on Nature and Biologically Inspired Computing (NaBIC 2009),
Dec 2009, India. IEEE Publications, USA, pp. 210-224.
- A. A. Mohamed, Y. S. Mohamed, A. A. M. El-Gaafary and A. M. Hemeida,
Optimal power flow using moth swarm algorithm, Electric Power System
Research, Vol. 142, 2017, pp. 190-206.
- P. Civicioglu, Backtracking search optimization algorithm for
numerical optimization problems, Applied Mathematics and Computation,
Vol. 219, 2013, pp. 8121-8144.
- R.V. Rao, V.J. Savsani and D.P. Vakharia, Teaching-learning-based
optimization: anovel method for constrained mechanical design
optimization problems, Comput. Aided Des. Vol 43 Iss. 3, 2011, pp.
303–315.
- H. R. E. H. Bouchekara, M. A. Abido and M. Boucherma, Optimal power
flow using Teaching-Learning-Based Optimization technique, Electr.
Power Syst. Res., Vol. 114, 2014, pp. 49-59.