Full Text:   <6437>

CLC number: TM73; TM74

On-line Access: 


Revision Accepted: 2004-08-07

Crosschecked: 0000-00-00

Cited: 22

Clicked: 8047

Citations:  Bibtex RefMan EndNote GB/T7714

-   Go to

Article info.
1. Reference List
Open peer comments

Journal of Zhejiang University SCIENCE A 2005 Vol.6 No.5 P.420-427


Multiple objective particle swarm optimization technique for economic load dispatch

Author(s):  ZHAO Bo, CAO Yi-jia

Affiliation(s):  School of Electrical Engineering, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   yijiacao@zju.edu.cn

Key Words:  Economic load dispatch, Multi-objective optimization, Multi-objective particle swarm optimization

ZHAO Bo, CAO Yi-jia. Multiple objective particle swarm optimization technique for economic load dispatch[J]. Journal of Zhejiang University Science A, 2005, 6(5): 420-427.

@article{title="Multiple objective particle swarm optimization technique for economic load dispatch",
author="ZHAO Bo, CAO Yi-jia",
journal="Journal of Zhejiang University Science A",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Multiple objective particle swarm optimization technique for economic load dispatch
%A CAO Yi-jia
%J Journal of Zhejiang University SCIENCE A
%V 6
%N 5
%P 420-427
%@ 1673-565X
%D 2005
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2005.A0420

T1 - Multiple objective particle swarm optimization technique for economic load dispatch
A1 - ZHAO Bo
A1 - CAO Yi-jia
J0 - Journal of Zhejiang University Science A
VL - 6
IS - 5
SP - 420
EP - 427
%@ 1673-565X
Y1 - 2005
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2005.A0420

A multi-objective particle swarm optimization (MOPSO) approach for multi-objective economic load dispatch problem in power system is presented in this paper. The economic load dispatch problem is a non-linear constrained multi-objective optimization problem. The proposed MOPSO approach handles the problem as a multi-objective problem with competing and non-commensurable fuel cost, emission and system loss objectives and has a diversity-preserving mechanism using an external memory (call “repository”) and a geographically-based approach to find widely different Pareto-optimal solutions. In addition, fuzzy set theory is employed to extract the best compromise solution. Several optimization runs of the proposed MOPSO approach were carried out on the standard IEEE 30-bus test system. The results revealed the capabilities of the proposed MOPSO approach to generate well-distributed Pareto-optimal non-dominated solutions of multi-objective economic load dispatch. Comparison with Multi-objective Evolutionary Algorithm (MOEA) showed the superiority of the proposed MOPSO approach and confirmed its potential for solving multi-objective economic load dispatch.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


[1] Coello, C.A.C., 1999. A comprehensive survey of evolutionary-based multiobjective optimization techniques. Knowledge and Information Systems, 1(3):129-156.

[2] Coello, C.A.C., Lechuga, M.S., 2003. Mopso: A proposal for multiple objective particle swarm optimization. IEEE Proceedings World Congress on Computational Intelligence, 2:1051-1056.

[3] Das, D.B., Patvardhan, C., 1998. New multi-objective stochastic search technique for economic load dispatch. IEE Proceedings(Generation, Transmission and Distribution, 145(6):747-752.

[4] Dhillon, J.S., Parti, S.C., Kothari, D.P., 1993. Stochastic economic emission load dispatch. Electrical Power Systems Research, 26:179-186.

[5] El-Keib, A.A., Ma, H., Hart, J.L., 1994. Economic dispatch in view of the clean air act of 1990. IEEE Transactions on Power Systems, 9(2):972-978.

[6] Fieldsend, J.E., Singh, S., 2002. A Multi-Objective Algorithm Based upon Particle Swarm Optimization, An Efficient Data Structure and Turbulence. The 2002 U.K. Workshop on Computational Intelligence, p.34-44.

[7] Fieldsend, J.E., Everson, R.M., Singh, S., 2003. Using unconstrained elite archives for multi-objective optimization. IEEE Transactions on Evolutionary Computation, 7(3):305-323.

[8] Goldberg, D.E., 1989. Genetic Algorithms for Search, Optimization, and Machine Learning. Addison-Wesley Professional/Pearson, New York.

[9] Hsiao, Y.T., Chiange, H.D., Liu, C.C., Chen, Y.L., 1994. A computer package for optimal multi-objective VAR planning in large scale power systems. IEEE Transactions on Power Systems, 9(2):668-676.

[10] Huang, C.M., Yang, H.T., Huang, C.L., 1997. Bi-objective power dispatch using fuzzy satisfaction-maximizing decision approach. IEEE Transactions on Power Systems, 12(4):1715-1721.

[11] Kennedy, J., Eberhart, R., 1995. Particle Swarm Optimization. Proceedings of IEEE International Conference on Neural Networks, p.1942-1948.

[12] Knowles, J.D., Corne, D., 2000. Approximating the nondominated front using the pareto archived evolution strategy. Evolutionary Computation, 8(2):149-172.

[13] Niimura, T., Nakashima, T., 2003. Multiobjective tradeoff analysis of deregulated electricity transactions. International Journal of Electrical Power & Energy Systems, 25(3):179-185.

[14] Parsopoulos, K.E., Vrahatis, M.N., 2002. Particle Swarm Optimization Method in Multi-Objective Problems. Proceedings of the 2002 ACM Symposium on Applied Computing, Madrid, Spain, p.603-607.

[15] Srinivasan, D., Chang, C.S., Liew, A.C., 1994. Multiobjective Generation schedule using fuzzy optimal search technique. IEE Proceedings(Generation, Transmission and Distribution, 141(3):233-242.

[16] Zimmerman, R., Gan, D., 1997. MATPOWER: A Matlab Power System Simulation Package. Available: http://www.pserc.cornell.edu/matpower.

Open peer comments: Debate/Discuss/Question/Opinion



2014-11-24 00:31:39

i need this file

Please provide your name, email address and a comment

Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE