skip to main content
10.1145/1276958.1277343acmconferencesArticle/Chapter ViewAbstractPublication PagesgeccoConference Proceedingsconference-collections
Article

Real-coded ECGA for economic dispatch

Published: 07 July 2007 Publication History

Abstract

In this paper, we propose a new approach that consists of the extended compact genetic algorithm (ECGA) and split-on-demand (SoD), an adaptive discretization technique, to economic dispatch (ED) problems with nonsmooth cost functions. ECGA is designed for handling problems with decision variables of the discrete type, while the decision variables of ED problems are oftentimes real numbers. Thus, in order to employ ECGA to tackle ED problems, SoD is utilized for discretizing the continuous decision variables and works as the interface between ECGA and the ED problem. Furthermore, ED problems in practice are usually hard for traditional mathematical programming methodologies because of the equality and inequality constraints. Hence, in addition to integrating ECGA and SoD, in this study, we devise a repair operator specifically for making the infeasible solutions to satisfy the equality constraint. To examine the performance and effectiveness, we apply the proposed framework to two different-sized ED problems with nonsmooth cost function considering the valve-point effects. The experimental results are compared to those obtained by various evolutionary algorithms and demonstrate that handling ED problems with the proposed framework is a promising research direction.

References

[1]
J. W. Allen and F. W. Bruce. Power Generation, Operation, and Control. New York: Wiley, 1984.
[2]
A. G. Bakirtzis, P. N. Biskas, C. E. Zoumas, and V. Petridis. Optimal power flow by enhance genetic algorithm. IEEE Transactions on Power Systems, 17:229--236, May 2002.
[3]
C.-H. Chen, W.-N. Liu, and Y.-p. Chen. Adaptive discretization for probabilistic model building genetic algorithms. In Proceedings of ACM SIGEVO Genetic and Evolutionary Computation Conference (GECCO-2006), pages 1103--1110, 2006.
[4]
P.-H. Chen and H.-C. Chang. Large-scale economic dispatch by genetic algorithm. IEEE Transactions on Power Systems, 10:1919--1926, February 1995.
[5]
G. R. Harik. Linkage learning via probabilistic modeling in the ECGA. IlliGAL Report No. 99010, University of Illinois at Urbana-Champaign, Illinois Genetic Algorithms Laboratory, Urbana, IL, 1999.
[6]
P.-C. Hung and Y.-p. Chen. iECGA: Integer extended compact genetic algorithm. In Proceedings of ACM SIGEVO Genetic and Evolutionary Computation Conference (GECCO-2006), pages 1415--1416, 2006.
[7]
W. M. Lin, F. S. Cheng, and M. T. Tsay. An improved tabu search for economic dispatch with multiple minima. IEEE Transactions on Power Systems, 17(1):108--112, 2002.
[8]
J. B. Park, K. S. Lee, J. R. Shin, and K. Y. Lee. A particle swarm optimization for economic dispatch with nonsmooth cost functions. IEEE Transactions on Power Systems, 20(1):34--42, 2005.
[9]
Y.-M. Park, J. R. Won, and J. B. Park. New approach to economic load dispatch based on improved evolutionary programming. Eng. Intell. Syst. Elect. Eng. Commun, 6(2):103--110, June 1998.
[10]
J. Rissanen. Stochastic Complexity in Statistical Inquiry. World Science, 1989.
[11]
G. B. Sheble and K. Brittig. Refined genetic algorithm-economic dispatch example. IEEE Transactions on Power Systems, 10:117--124, November 1995.
[12]
N. Sinha, R. Chakrabarti, and P. K. Chattopadhyay. Evolutionary programming techniques for economic load dispatch. IEEE Transactions on Evolutionary Computation, 7(1):83--94, February 2003.
[13]
K. S. Swarup and S. Yamashiro. Unit commitment solution methodologies using genetic algorithm. IEEE Transactions on Power Systems, 17:87--91, February 2002.
[14]
D. C. Walters and G. B. Sheble. Genetic algorithm solution of economic dispatch with valve point loading. IEEE Transactions on Power Systems, 8(3):1325--1332, August 1993.
[15]
H. T. Yang, P. C. Yang, and C. L. Huang. Evolutionary programming based economic dispatch for units with non-smooth fuel cost functions. IEEE Transactions on Power Systems, 11(1):112--117, 1996.

Cited By

View all
  • (2019)A hybrid evolutionary approach based on the invasive weed optimization and estimation distribution algorithmsSoft Computing10.1007/s00500-019-03902-xOnline publication date: 20-Mar-2019
  • (2016)Trend Prediction Model Based Multi-Objective Estimation of Distribution AlgorithmArtificial Intelligence and Robotics Research10.12677/AIRR.2016.5100105:01(1-12)Online publication date: 2016
  • (2014)Evolving Mixtures of n-gram Models for Sequencing and Schedule OptimizationParallel Problem Solving from Nature – PPSN XIII10.1007/978-3-319-10762-2_31(312-321)Online publication date: 2014
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
July 2007
2313 pages
ISBN:9781595936974
DOI:10.1145/1276958
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 July 2007

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. ECGA
  2. SoD
  3. adaptive discretization
  4. economic dispatch
  5. genetic algorithm
  6. split-on-demand
  7. valve-point effect

Qualifiers

  • Article

Conference

GECCO07
Sponsor:

Acceptance Rates

GECCO '07 Paper Acceptance Rate 266 of 577 submissions, 46%;
Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)7
  • Downloads (Last 6 weeks)1
Reflects downloads up to 16 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2019)A hybrid evolutionary approach based on the invasive weed optimization and estimation distribution algorithmsSoft Computing10.1007/s00500-019-03902-xOnline publication date: 20-Mar-2019
  • (2016)Trend Prediction Model Based Multi-Objective Estimation of Distribution AlgorithmArtificial Intelligence and Robotics Research10.12677/AIRR.2016.5100105:01(1-12)Online publication date: 2016
  • (2014)Evolving Mixtures of n-gram Models for Sequencing and Schedule OptimizationParallel Problem Solving from Nature – PPSN XIII10.1007/978-3-319-10762-2_31(312-321)Online publication date: 2014
  • (2013)A multi-Gaussian component EDA with restarting applied to direction of arrival tracking2013 IEEE Congress on Evolutionary Computation10.1109/CEC.2013.6557747(1556-1563)Online publication date: Jun-2013
  • (2012)Economic Load Dispatch with Prohibited Operating Zones Using Genetic AlgorithmsProceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011) December 20-22, 201110.1007/978-81-322-0487-9_59(611-619)Online publication date: 15-Apr-2012
  • (2011)An introduction and survey of estimation of distribution algorithmsSwarm and Evolutionary Computation10.1016/j.swevo.2011.08.0031:3(111-128)Online publication date: Sep-2011
  • (2008)Adaptive discretization on multidimensional continuous search spacesProceedings of the 10th annual conference on Genetic and evolutionary computation10.1145/1389095.1389280(977-984)Online publication date: 13-Jul-2008
  • (2008)Real-Coded Extended Compact Genetic Algorithm Based on Mixtures of ModelsLinkage in Evolutionary Computation10.1007/978-3-540-85068-7_14(335-358)Online publication date: 2008

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media