This paper presents an application of Cuckoo search algorithm to determine optimal location and sizing of Static VAR Compensator. Cuckoo search algorithm is a modern heuristic technique basing Cuckoo species’ parasitic strategy. The Lévy flight has been employed to generate random Cuckoo eggs. Moreover, the objective function is a multiobjective problem, which minimizes loss power, voltage deviation and investment cost of Static VAR Compensator while satisfying other operating constraints in power system. Cuckoo search algorithm is evaluated on three case studies and compared with the Teaching-learning-based optimization, Particle Swarm optimization and Improved Harmony search algorithm. The results show that Cuckoo search algorithm is better than other optimization techniques and its performance is also better.
 Del Valle, Y., Hernandez, J. C., Venayagamoorthy, G. K., & Harley, R. G., Optimal STATCOM Sizing and Placement Using Particle Swarn Optimization. In Transmission & Distribution Conference and Exposition: Latin America. TDC’06. IEEE/PES. IEEE, 2006, pp. 1-6.
 Chao-Ming, H., Yann-Chang H., Kun-Yuan H. & Hong-Tzer Y., A Harmony Search Algorithm for Optimal Power Flow Considering Flexible AC Transmission Systems, Intelligent Systems Applications to Power Systems, ISAP 2013. International Conference on, 2013, pp. 1-6
 Pisica, I., Bulac, C., Toma, L., & Eremia, M., Optimal SVC placement in electric power systems using a genetic algorithms based method. In PowerTech, IEEE Bucharest. IEEE, 2009, pp. 1-6.
 Sirjani, R., Mohamed, A., & Shareef, H., Optimal placement and sizing of Static Var Compensators in power systems using Improved Harmony Search Algorithm. Przeglad Elektrotechniczny, 87(7), 2011, 214-218.
 Sirjani, R., Mohamed, A., & Shareef, H., Optimal allocation of shunt Var compensators in power systems using a novel global harmony search algorithm. International Journal of Electrical Power & Energy Systems, 43(1), 2012, 562-572.
 Yang, X. S.,& Deb, S., Cuckoo search via L´evy flights. In Nature & Biologically Inspired Computing, 2009. NaBIC 2009. World Congress on (pp. 210-214). IEEE.
 Yang, X. S., & Deb, S., Engineering optimization by cuckoo search. International Journal of Mathematical Modelling and Numerical Optimisation, 1(4), 2010, 330-343.
 Civicioglu, P., & Besdok, E., A conceptual comparison of the Cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms. Artificial Intelligence Review, 39(4), 2013, 315-346.
 Moravej, Z., & Akhlaghi, A., A novel approach based on cuckoo search for DG allocation in distribution network. International Journal of Electrical Power & Energy Systems, 44(1), 2013, 672-679.
 Ahmed, J., & Salam, Z., A Maximum Power Point Tracking (MPPT) for PV system using Cuckoo Search with partial shading capability. Applied Energy, 119, 2014, 118-130.
 Vo, D. N., Schegner, P., & Ongsakul, W., Cuckoo search algorithm for non-convex economic dispatch. Generation, Transmission & Distribution, IET,7(6), 2013.
 Cai, L. J., Erlich, I., & Stamtsis, G., Optimal choice and allocation of FACTS devices in deregulated electricity market using genetic algorithms. In Power Systems Conference and Exposition, 2004. IEEE PES(pp. 201-207). IEEE.
 Mantegna, R. N., Fast, accurate algorithm for numerical simulation of Levy stable stochastic processes. Physical Review E, 49(5), 1994, 4677.
 Zimmerman, R. D., Murillo-Snchez, C. E., & Thomas, R. J., MATPOWER: Steady-state operations, planning, and analysis tools for power systems research and education. Power Systems, IEEE Transactions on,26(1), 2011, 12-19.
 Rao, R. V., Savsani, V. J., & Vakharia, D. P., Teachinglearning-based optimization: an optimization method for continuous non-linear large scale problems. Information Sciences, 183(1), 2012,1-15.
 Rao, R., & Patel, V. (2012). An elitist teachinglearning- based optimization algorithm for solving complex constrained optimization problems. International Journal of Industrial Engineering Computations, 3(4), 535-560.
 Ratnaweera, A., Halgamuge, S. K., & Watson, H. C. (2004). Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients.Evolutionary Computation, IEEE Transactions on, 8(3), 240-255.
 Mahdavi, M., Fesanghary, M., & Damangir, E. (2007). An improved harmony search algorithm for solving optimization problems. Applied mathematics and computation, 188(2), 1567-1579