CFP last date

Published on March 2014 by
Parmar K.P.Singh,
Bhuvnesh Khokhar

International Conference on Advances in Computer Engineering and Applications |

Foundation of Computer Science USA |

ICACEA - Number 2 |

March 2014 |

Authors: Parmar K.P.Singh, Bhuvnesh Khokhar |

115b6168-e979-4e31-a020-c76557afce70 |

Parmar K.P.Singh, Bhuvnesh Khokhar . Oppositional Biogeography-Based Optimization for Solving Economic Dispatch Problems: An Efficient Method. International Conference on Advances in Computer Engineering and Applications. ICACEA, 2 (March 2014), 53-58.

@article{

author = {
Parmar K.P.Singh,
Bhuvnesh Khokhar
},

title = { Oppositional Biogeography-Based Optimization for Solving Economic Dispatch Problems: An Efficient Method },

journal = {
International Conference on Advances in Computer Engineering and Applications
},

issue_date = { March 2014 },

volume = { ICACEA },

number = { 2 },

month = { March },

year = { 2014 },

issn = 0975-8887,

pages = {
53-58
},

numpages = 6,

url = {
/proceedings/icacea/number2/15622-1417/
},

publisher = {Foundation of Computer Science (FCS), NY, USA},

address = {New York, USA}

}

%0 Proceeding Article

%1 International Conference on Advances in Computer Engineering and Applications

%A Parmar K.P.Singh

%A Bhuvnesh Khokhar

%T Oppositional Biogeography-Based Optimization for Solving Economic Dispatch Problems: An Efficient Method

%J International Conference on Advances in Computer Engineering and Applications

%@ 0975-8887

%V ICACEA

%N 2

%P 53-58

%D 2014

%I International Journal of Computer Applications

In this paper, Oppositional biogeography-based optimization (OBBO) technique based on opposition-based learning (OBL) concept has been presented for solving the economic dispatch (ED) problems. The OBBO technique has been applied on two test systems, one consisting of three generators and the other of six generators. The results obtained have been compared with the conventional Lagrange multiplier method, particle swarm optimization (PSO) and biogeography-based optimization (BBO) methods. The results show that the presented OBBO technique has good convergence characteristics and provides comparatively better solutions in terms of total fuel cost as compared to other methods. Also, the global search capability is enhanced and premature convergence is avoided.

- D. P. Kothari, and J. S. Dhillon, ‘Power System Optimization’, 2nd edition, PHI, New Delhi, 2010
- Mohamed-Nor, K. and A. H. A. Rashid, ‘Efficient economic dispatch algorithm for thermal unit commitment’, IEE Proceedings, vol. 138 (3), pp. 213-217, 1991
- Chung-Lung, Chen and Nanming Chen, ‘Direct search method for solving economic dispatch problem considering transmission capacity constraints’, IEEE Trans. on Power Systems, vol. PWRS-16 (4), pp. 764-769, 2001
- Y. S. Brar, J. S. Dhillon, and D. P. Kothari, ‘Multi-objective load dispatch by fuzzy logic searching weightage pattern’, Electric Power Systems Research, vol. 63 (2), pp. 149-160, 2002
- K. Y. Lee, A. Sode-Yome, and J. H. Park, ‘Adaptive Hopfield neural networks for economic load dispatch’, IEEE Trans. on Power Systems, vol. 13 (2), pp. 519-526, 1998
- T. Yalcinoz, and M. J. Short, ‘Neural networks approach for solving economic dispatch problem with transmission capacity constraints’, IEEE Trans. on Power Systems, vol. 13, pp. 307-313, 1998
- D. C. Walter, and G. B. Sheble, ‘Genetic algorithm solution of economic dispatch with valve-point loading’, IEEE Trans. on Power Systems, vol. 8 (3), pp. 1325-1332, 1993
- G. B. Sheble, and K. Brittig, ‘Redefined genetic algorithm – economic ]dispatch example’, IEEE Trans. on Power Systems, vol. 10, pp. 117-124, 1995
- D. B. Fogel, ‘Evolutionary computation: Towards a new philosophy of machine intelligence’, 2nd edition, Piscataway, NJ: IEEE Press, 2000
- R. C. Eberhart, and Y. Shi, ‘Comparison between genetic algorithms and particle swarm optimization’, Proceedings of IEEE Int. Conf. on Evol. Comput., pp. 611-616, 1998
- Z. L. Gaing, ‘Particle swarm optimization to solve the economic dispatch considering the generator constraints’, IEEE Trans. on Power Systems, vol. 18 (3), pp. 1187-1195, 2003
- A. Ratnaweera, S. K. Halgamuge, and H. C. Watson, ‘Self-organizing hierarchical particle swarm optimizer with time varying acceleration coefficients, IEEE Trans. on Evol.Comput., vol. 8 (3), pp. 240-255, 2004
- A. I. Selvakumar, and K. Thanushkodi, ‘A new particle swarm optimization solution to non-convex economic dispatch problems, IEEE Trans. on Power Systems, vol. 22 (1), pp. 42-51,2007
- B. K. Panigrahi, and V. R. Pandi, ‘Bacterial foraging optimization: Nelder-Mead algorithm for economic load dispatch’, Generation, Transmission and Distribution, IET, vol. 2 (4), pp. 556-565, 2008
- Nasimul Noman, and Hitoshi Iba, ‘Differential evolution for economic load dispatch problems’, Electric Power Systems Research, vol. 78, pp. 1322-1331, 2008
- E. Mezura-Montes, J. Velazquez-Reyes, and C.A.C. Coello, ‘Modified differential evolution for constrained optimization’, Proceedings of the 2006 IEEE Congress on Evolutionary Computation, pp. 332– 339, 2006
- D. Simon, ‘Biogeography-based optimization’, IEEE Trans. on Evol. Comput., vol. 12 (6), pp. 702-713, 2008
- H. Tizhoosh, ‘Opposition-based learning: A new scheme for machine intelligence’, Proc. of Int. Conf. on Computer Intelligence for Modeling Control and Automation, vol. 1, pp. 695-701, 2005
- M. Ergezer, D. Simon, and D. Du, ‘Oppositional biogeography-based optimization’, IEEE International Conf. on Systems, Man and Cybernetics SMC 2009, pp. 1009-1014, 2009
- S. Rahnamayan, H. Tizhoosh, and M. Salama, ‘Quasi-opposition differential evolution’, Proc. in IEEE Congress on Evol.Comput. CEC 2007, pp. 2229-2236, 2007
- M. Ventresca, and H. Tizhoosh, ‘Improving the convergence of back-propagation by opposite transfer functions’, in IEEE Int. Joint Conf. on Neural Networks, pp. 9527-9534, 2006
- A. Bhattacharya, and P. K. Chattopadhyay, ‘Solution of economic power dispatch problems using oppositional biogeography-based optimization’, Electric Power Components and Systems, vol. 38, pp. 1139-1160, 2010
- Bhuvnesh Khokhar, and K. P. Singh Parmar, ‘A novel weight-improved particle swarm optimization for combined economic and emission dispatch problems’, IJEST, vol. 4(5), pp. 2008-2014, 2012
- Bhuvnesh Khokhar, K. P. Singh Parmar, and Surender Dahiya, ‘Application of Biogeography-based optimization for economic dispatch problems’, IJCA, vol. 47(13), pp. 25-30, 2012
- A. Bhattacharya, and P. K. Chattopadhyay, ‘Solving complex economic load dispatch problems using biogeography based optimization’, Expert Systems with Applications, vol. 37(5), pp. 3605-3615, 2010
- P K Roy, S P Ghoshal, and S S Thakur, ‘Biogeography-based optimization for economic load dispatch problems’, Electric Power Components and Systems, vol. 38(2), pp. 166-181, 2009
- Bhuvnesh Khokhar, and K.P. Singh Parmar, ‘An efficient particle swarm optimization with time varying acceleration coefficients to solve economic dispatch problem with valve point loading’, Journal of Energy and Power, vol. 2(4), pp. 74-80, 2012
- Naveen Kumar, K.P.Singh Parmar, and Surender Dahiya, ‘Optimal Solution of Combined Economic Emission Load Dispatch using Genetic Algorithm’, International Journal of Computer Applications, vol 48(15), pp15-20, 2012.

Computer Science

Information Sciences