1. Ali Ahrari,Ali Atai (2010). Grenade Explosion Method—A novel tool for optimization of multimodal functions. Applied Soft Computing, 10(4), 1132-1140.
2. Dorigo M (1992). Optimization, learning and natural algorithms.
3. Eusuff M,Lansey E (2003). Optimization of Water Distribution Network Design Using the Shuffled Frog Leaping Algorithm. pp, 129, 210-225.
4. J. Farmer,Norman Packard,Alan Perelson (1986). The immune system, adaptation, and machine learning. pp, 22(1-3), 187-204.
5. Z. Geem,J. Kim,G. Loganathan (2001). A new heuristic optimization algorithm: Harmony Search. Simul, the Soc for Model and Simul Int, 76(2), 60-68.
6. J. Holland (1975). Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor
7. D. Karaboga (2005). An idea based on honey bee swarm for numerical optimization. Technical Report- TR06, Erciyes University, Engineering Faculty, Computer Engineering Department, Turkey
8. J. Kennedy,R. Eberhart (1995). Particle swarm optimization. Proceedings of ICNN'95 - International Conference on Neural Networks, 4
9. K. Passino (2002). Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Systems, 22(3), 52-67.
10. Rashedi E,Nezamabadi-pour H,Saryazdi S (2009). GSA: A Gravitational Search Algorithm. pp, 179, 2232-2248.
11. D. Simon (2008). Biogeography-Based Optimization. pp, 12, 702-713.
12. Rainer Storn,Kenneth Price (1997). Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces. Journal of Global Optimization, 11(4), 341-359.
13. Preechakul C,Kheawhom S (2009). Modified genetic algorithm with sampling techniques for chemical engineering optimization. pp, 101-107.
14. Hao Cui,Osman Turan (2010). Application of a new multi-agent Hybrid Co-evolution based Particle Swarm Optimisation methodology in ship design. Computer-Aided Design, 42(11), 1013-1027.
15. Montalvo I,Izquierdo J,Perez-Garcia R,Herrera M (2010). Improved performance of PSO with self-adaptive parameters for computing the optimal design of Water Supply Systems. pp, 23, 727-735.
16. P. Ying (2010). An ant direction hybrid differential evolution algorithm in determining the tilt angle for photovoltaic modules. Expert Sys Appl, 37, 5415-5422.
17. Hualong Yu,Guochang Gu,Haibo Liu,Jing Shen,Jing Zhao (2009). A Modified Ant Colony Optimization Algorithm for Tumor Marker Gene Selection. Genomics, Proteomics & Bioinformatics, 7(4), 200-208.
18. Dervis Karaboga,Bahriye Akay (2010). A modified Artificial Bee Colony (ABC) algorithm for constrained optimization problems. Applied Soft Computing, 11(3), 3021-3031.
19. Fsa Mouti,Mee Hawary (2009). Modified artificial bee colony algorithm for optimal distributed generation sizing and allocation in distribution systems. IEEE Electr Power and Energy Conf (EPEC, 1-9.
20. L. Hui,C. Zixing,Yong (2010). Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization. Appl Soft Comput, 629-640.
21. Y. Wen (2010). A GA-DE hybrid evolutionary algorithm for path synbook of four-bar linkage. Mech Mach Theory, 45, 1096-1107.
22. K. Chandrasekar,N. Ramana (2011). Performance comparison of DE, PSO and GA approaches in Transmission Power Loss minimization using FACTS Devices. International Journal of Computer Applications, Volume 33– No.5
23. Abouzar Samimi,Peyman Naderi (2012). A New Method for Optimal Placement of TCSC Based on Sensitivity Analysis for Congestion Management. Smart Grid and Renewable Energy, 3, 10-16.
24. G. Rashed,H. Shaheen,X. Duan,S. Cheng (2008). Evolutionary optimization techniques for optimal location and parameter setting of TCSC under single line contingency. Applied Mathematics and Computation, 205(1), 133-147.
25. T. Tjing,Wanhong Lie,Deng (1997). Optimal Flexible AC Transmission Systems (FACTS)devices allocation. Electrical Power & Energy Systems, 19(2), 125-134.
26. G. Shaoyun,T. Chung (1998). Optimal active power flow incorporating FACTS devices with power flow control constraints. International Journal of Electrical Power & Energy Systems, 20(5), 321-326.
27. Sidhartha Panda,Narayana Padhy (2008). Comparison of particle swarm optimization and genetic algorithm for FACTS-based controller design. Applied Soft Computing, 8(4), 1418-1427.
28. B. Bhattacharyya,Vikash Gupta (2012). Application of DE & PSO Algorithm for the Placement of FACTS Devices for Economic Operation of a Power System. pp, 2012, 209-216.
29. T. Chung,Y. Li (2001). A hybrid GA approach for OPF with consideration of FACTS devices. IEEE Power Engineering Review, 20(8), 54-57.
30. S. Ge,T. Chung (1999). Optimal active power flow incorporating power flow control needs in flexible AC transmission systems. IEEE Transactions on Power Systems, 14(2), 738-744.
31. G. Venkata Lakshmi,K. Amaresh (2012). Optimal Power Flow with TCSC using Genetic Algorithm. IEEE International Conference on Power Electronics, Drives and Energy Systems
32. A. Alabduljabbar,J. Milanović (2010). Assessment of techno-economic contribution of FACTS devices to power system operation. EPSR, 80, 1247-1255.
33. Atashpaz-Gargari E,Lucas C (2007). Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition. 2007 IEEE Congress on Evolutionary Computation, 2007, 4661-4667.
