Economic Dispatch Optimization Using Imperialist Competitive Algorithm (ICA) and compare with PSO algorithm result
DOI:
https://doi.org/10.24297/ijct.v15i2.3981Keywords:
Imperialist Competitive Algorithm (ICA), Particle Swarm Optimization (PSO), Economic Dispatch Problem (EDP)Abstract
Measurement Imperialist Competitive Algorithm (ICA) is a population based stochastic optimization technique, originallydeveloped by Eberhart and Kennedy, inspired by simulation of a social psychological metaphor instead of the survival of the fittest individual. In ICA, the system (imperialists) is initialized with a population of random solutions (colonies) and searches for optimal using cognitive and social factors by updating generations. ICA has been successfully applied to a wide range of applications, mainly in solving continuous nonlinear optimization problems. Based on the ICA, this paper discusses the use of ICA approach to optimize performance of economic dispatch problems. The proposed method is validated for a test system consisting of 13 thermal units whose incremental fuel cost function takes into account the valve-point loading effects
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Transmission, and Distribution, vol. 152, no. 5, pp. 653-660, 2005.
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economic dispatch optimization with valve-point effect,†IEEE Transactions on Power Systems, vol. 21, no. 2, pp.
989-996, 2006.
[3] T. Yalcinoz and H. Altun, “Power economic dispatch using a hybrid genetic algorithm,†IEEE Power Engineering
Review, March, pp. 59-60, 2001.
[4] Y. H. Song and C. S. V. Chou, “Large-scale economic dispatch by artificial ant colony search algorithms,†Electric
Machines and Power Systems, vol. 27, pp. 679-690, 1999.
[5] M. Basu, “A simulated annealing-based goal-attainment method for economic emission load dispatch of fixed head
hydrothermal power systems,†International Journal of Electrical Power & Energy Systems, vol. 27, no. 2, pp. 147-
153, 2005.
[6] Balakrishnan, P. S. Kannan, C. Aravindan and P. Subathra, â€On-line emission and economic load dispatch using
adaptive Hopfield neural network,†Applied Soft Computing, vol. 2, no. 4, pp. 297-305, 2003.
[7] E. Atashpaz_gargari, c. Lucas, Imprrialist Competitive Algorithm: an algorithm for optimization inspired by
imperialistic competition, (ICA) IEEE CEC 2007.
[8] N. Sinha, R. Chakrabarti, and P. K. Chattopadhyay, “Evolutionary programming techniques for economic load
dispatch,†IEEE Transactions on Evolutionary Computation, vol. 7, no. 1, pp. 83-94.
[9] D. Stoneking, G. Bilbro, R. Trew, P. Gilmore, and C. T. Kelley, “Yield optimization using a GaAs process simulator
coupled to a physical device model,†Proceedings IEEE/Cornell Conference on Advanced Concepts in High Speed
Devices and Circuits, Ithaca, NY, USA, pp. 374-383, 1991.
[10] D. B. Bertsekas, “On the Goldstein-Levitin-Polyak gradient projection method,†IEEE Transactions on Automatic
Control, vol. 21, no. 2, pp. 74-184, 1976.
[11] P. Gilmore and C. T. Kelley, “An implicit filtering algorithm for optimization of functions with many local minima,â€
SIAM Journal of Optimization, vol. 5, no. 2, pp. 269-275, 1995.
[12] P. Gilmore and C. T. Kelley, “An implicit filtering algorithm for optimization of functions with many local minima,â€
Technical Report CRSC-TR94-23, North Carolina State University, Center for Research in Scientific Computation,
USA, 1994.
[13] K. P. Wong and Y. W. Wong, “Genetic and genetic/simulated-annealing approaches to economic dispatch,†IEE
Proc. Control, Generation, Transmission and Distribution, vol. 141, no. 5, pp. 507-513, 1994.
[14] T. D. Choi, O. J. Eslinger, C. T. Kelley, J. W. David, and M. Etheridge, â€Optimization of automotive valve train
components with implicit filtering,†Optimization and Engineering, vol. 1, no. 1, pp. 9-27, 2000.
[15] Leandro dos Santos Coelho and Viviana Cocco Mariani, â€Economic Dispatch Optimization Using Hybrid Chaotic
Particle Swarm Optimizer,â€1-4244-0991-8/07/$25.00/©2007 IEEE.
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Published
2015-12-07
How to Cite
Jalilzadeh, S., & Nikkhah, S. (2015). Economic Dispatch Optimization Using Imperialist Competitive Algorithm (ICA) and compare with PSO algorithm result. INTERNATIONAL JOURNAL OF COMPUTERS &Amp; TECHNOLOGY, 15(2), 6541–6545. https://doi.org/10.24297/ijct.v15i2.3981
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