• Dr.K.Lenin Professor, Department of EEE, Prasad V.Potluri Siddhartha Institute of Technology, Kanuru, Vijayawada, Andhra Pradesh -520007, India



Symbiosis modeling Hybridization of Evolutionary algorithm with conventional Algorithm, Genetical Swarm Optimization, Reactive Power Optimization

Abstract [English]

This paper presents assorted algorithms for solving optimal reactive power problem. Symbiosis modeling (SM), which extends the dynamics of the canonical PSO algorithm by adding a significant ingredient that takes into account the symbiotic co evolution between species, Hybridization of  Evolutionary  algorithm with Conventional Algorithm (HCA) that uses the abilities of evolutionary and conventional algorithm and Genetical Swarm Optimization (GS), which combines Genetic Algorithms (GA) and Particle Swarm Optimization (PSO).All the above said  SM, HCA,GS algorithms are used to  augment the convergence rate with good Exploration & Exploitation. All the three SM, HCA, GS is applied to Reactive Power optimization problem and has been evaluated in standard IEEE 30 System. The results shows that all the three algorithms perform well in solving the reactive power problem with rapid convergence rate .Of all the three  algorithms SM has the slight edge in reducing the real power loss over  HCA&GS.


Download data is not yet available.


S.A. Frank, Models of symbiosis, American Naturalist 150 (1997) 80–99. DOI:

M.D. Jason, S.G. Catherine, A.S. Stephen, J.R. Steven, Symbionticism and complex adaptive systems I: implications of having symbiosis occur in nature,in: Proceedings of the 5th Annual Conference on Evolutionary Programming, Cambridge, 1996, pp. 177–186.

C.R. Marshall, Mass extinction probed, Nature 392 (1998) 17–20. DOI:

[4] M. Newman, Simple models of evolution and extinction, IEEE Computing in Science and Engineering (2000) 80–86.

Ben Niu, Yunlong Zhu, XiaoXian He, Xiangping Zeng, Henry Wu, MCPSO: a multi-swarm cooperative particle swarm optimizer, Applied Mathematicsand Computation 185 (2) (2007) 1050–1062

B. Niu, Y.L. Zhu, X.X. He, Multi-population cooperative particle swarm optimization, in: Proceedings ECAL2005, Lecture Notes in Computer Science No.3630, 2005, pp. 874–883.

J. Kennedy, Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance, in: Proceedings of the Congress onEvolutionary Computation, Piscataway, NJ, 1999, pp. 1931–1938.

J. Kennedy, R. Mendes, Population structure and particle swarm performance, in: Proceedings of the 2002 Congress on Evolutionary Computation,Piscataway, NJ, 2002, pp. 1671–1675.

P.N. Suganthan, Particle swarm optimizer with neighborhood operator, in: Proceedings of the Congress on Evolutionary Computation (CEC 1999),Piscataway, NJ, 1999, pp. 1958–1962.

X.H. Shi, Y.C. Liang, H.P. Lee, C. Lu, L.M. Wang, An improved GA and a novel PSO-GA-based hybrid algorithm, Information Processing Letters 93 (2005)255–261. DOI:

W.J. Zhang, X.F. Xie, DEPSO: Hybrid particle swarm with differential evolution operator, in: Proceedings of the IEEE International Conference onSystems, Man and Cybernetics, Washington, DC, USA, 2003, pp. 3816–3821.

B. Ye, C.Z. Zhu, C.X. Guo, Y.J. Cao, Generating extended Fuzzy basis function networks using hybrid algorithm, Lecture Notes in Artificial Intelligence3613 (2005) 79–88.

S. He, Q.H. Wu, J.Y. Wen, J.R. Saunders, R.C. Paton, A particle swarm optimizer with passive congregation, Biosystems 78 (2004) 35–147. DOI:

B. Niu, Y.L. Zhu, X.X. He, Multi-population cooperative particle swarm optimization, in: Proceedings ECAL2005, Lecture Notes in Computer Science No.3630, 2005, pp. 874–883.

M. Clerc, J. Kennedy, The particle swarm: explosion stability and convergence in a multidimensional complex space, IEEE Transactions on EvolutionaryComputation 6 (1) (2002) 58–73.

J. Kennedy,R. Eberhart, Particle swarm optimization, In:Proc IEEE intconftneural networks, vol.IV, Perth,Australia;1995. p.1942-48.

P. N. Suganthan, Particle Swarm Optimizer with Neighborhood Operator,i, in Proceedings of IEEE International Conference on EvolutionaryComputation.Washinington D.C. IEEE Press (1999), pp. 1958-1962.

A. Ratnaweera, S. K. Halgamuge, and H. C. Watson, Self-OrganizingHierarchical Particle Swarm Optimizer with Time-Varying AccelerationCoefficients, IEEE Transactions on Evolutionary Computation 8(3) 240-255 (2004). DOI:

D. W. Boeringer, and D. H. Werner, “Particle Swarm Optimization Versus Genetic Algorithms for Phased Array Synthesis,” IEEE Transactions on antennas and propagation, vol. 52, n. 3, pp. 771-779, March 2004. DOI:

J. Robinson, S. Sinton, and Y. Rahmat-Samii, “Particle swarm, genetic algorithm, and their hybrids: optimization of a profiled corrugated horn antenna,” Proc. of the 2002 IEEE AP-S International Symposium, San Antonio, TX, vol. 1, pp. 314-317, June 2002.

Chia-Feng Juang, “A Hybrid of Genetic Algorithm and Particle Swarm Optimization for Recurrent Network Design,” IEEE Transactions On Systems, Man And Cybernetics-Part B: Cybernetics, vol. 34, n. 2, pp. 997-1006, April 2004. DOI:

Wu.Q.H,Y.J.Cao,andJ.Y.Wen,(1998),“Optimal reactive power dispatch using an adaptive genetic algorithm”, Int.J.Elect.Power Energy Syst. Vol 20. Pp. 563-569.

Zhao.B,C.X.Guo,andY.J.CAO,(2005),“Multiagent-based particle swarm optimization approach for optimal reactive power dispatch”,IEEE Trans. Power Syst. Vol. 20, no. 2, pp. 1070-1078. DOI:

Mahadevan.K,KannanP.S,(2010)“Comprehensive Learning Particle Swarm Optimization for Reactive Power Dispatch”, Applied Soft Computing, Vol. 10, No. 2, pp. 641–52. DOI:

Khazali.A.H,M.Kalantar,(2011),“Optimal Reactive Power Dispatch based on Harmony Search Algorithm”, Electrical Power and Energy Systems, Vol. 33, No. 3, pp. 684–692. DOI:

Sakthivel.S,M.Gayathri,V.Manimozhi,(2013),“A Nature Inspired Optimization Algorithm for Reactive Power Control in a Power System”,


Tejaswini Sharma,Laxmi Srivastava,Shishir Dixit (2016). “Modified Cuckoo Search Algorithm For Optimal Reactive Power Dispatch”, Proceedings of 38 th IRF International Conference,pp4-8. 20th March, 2016, Chennai, India, ISBN: 978-93-85973-76-5.




How to Cite

Lenin, K. (2018). ACTIVE POWER LOSS REDUCTION BY ASSORTED ALGORITHMS. International Journal of Research -GRANTHAALAYAH, 6(5), 263–275.

Most read articles by the same author(s)

1 2 3 4 5 6 7 > >>