skip to main content
10.1145/2739482.2768471acmconferencesArticle/Chapter ViewAbstractPublication PagesgeccoConference Proceedingsconference-collections
short-paper

Differential Evolution with a Repair Method to Solve Dynamic Constrained Optimization Problems

Published: 11 July 2015 Publication History

Abstract

An algorithm inspired in two differential evolution variants is proposed to solve Dynamic Constrained Optimization Problems (DCOPs). It is also added a repair method based on the differential mutation, which does not require feasible solutions as reference. This approach is compared against state-of-the-art algorithms to solve DCOPs. Different performance measures are employed in the tests to show the competitiveness of our proposal at different change frequencies.

References

[1]
M.-Y. Ameca-Alducin, E. Mezura-Montes, and N. Cruz-Ramirez. Differential evolution with combined variants for dynamic constrained optimization. In Evolutionary Computation (CEC), 2014 IEEE Congress on, pages 975--982, July 2014.
[2]
V. Aragón, S. Esquivel, and C. Coello. Artificial immune system for solving dynamic constrained optimization problems. In E. Alba, A. Nakib, and P. Siarry, editors, Metaheuristics for Dynamic Optimization, volume 433 of Studies in Computational Intelligence, pages 225--263. Springer Berlin Heidelberg, 2013.
[3]
H. Cobb. An investigation into the use of hypermutation as an adaptive operator in genetic algorithms having continuous, time-dependent nonstationary environments. Technical report, Naval Research Lab Washington DC, 1990.
[4]
H. Cobb and J. Grefenstette. Genetic algorithms for tracking changing environments. In S. Forrest, editor, ICGA, pages 523--530. Morgan Kaufmann, 1993.
[5]
K. Deb. An efficient constraint handling method for genetic algorithms. Computer Methods in Applied Mechanics and Engineering, 186(24):311--338, 2000.
[6]
J. Derrac, S. García, D. Molina, and F. Herrera. A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm and Evolutionary Computation, 1(1):3--18, 2011.
[7]
S. Hernandez, G. Leguizamon, and E. Mezura-Montes. A hybrid version of differential evolution with two differential mutation operators applied by stages. In Evolutionary Computation (CEC), 2013 IEEE Congress on, pages 2895--2901, 2013.
[8]
E. Mezura-Montes, M. E. Miranda-Varela, and R. del Carmen Gómez-Ramón. Differential evolution in constrained numerical optimization. an empirical study. Information Sciences, 180(22):4223--4262, 2010.
[9]
Z. Michalewicz and G. Nazhiyath. Genocop iii: a co-evolutionary algorithm for numerical optimization problems with nonlinear constraints. In Evolutionary Computation, 1995., IEEE International Conference on, volume 2, pages 647--651 vol.2, Nov 1995.
[10]
T. Nguyen and X. Yao. Detailed experimental results of ga, riga, hyperm and ga + repair on the g24 set of benchmark problems. Technical report, School Comput. Sci., Univ. Birmingham, Birmingham, U.K., 2010. available at: http://www.staff.livjm.ac.uk /enrtngu1/Papers/DCOP fulldata.pdf.
[11]
T. Nguyen and X. Yao. Continuous dynamic constrained optimization: The challenges. IEEE Transactions on Evolutionary Computation, 16(6):769--786, 2012.
[12]
T. Nguyen and X. Yao. Evolutionary optimization on continuous dynamic constrained problems - an analysis. In S. Yang and X. Yao, editors, Evolutionary Computation for Dynamic Optimization Problems, volume 490 of Studies in Computational Intelligence, pages 193--217. Springer Berlin Heidelberg, 2013.
[13]
K. Pal, C. Saha, and S. Das. Differential evolution and offspring repair method based dynamic constrained optimization. In B. Panigrahi, P. Suganthan, S. Das, and S. Dash, editors, Swarm, Evolutionary, and Memetic Computing, volume 8297 of Lecture Notes in Computer Science, pages 298--309. Springer International Publishing, 2013.
[14]
K. Pal, C. Saha, S. Das, and C. Coello-Coello. Dynamic constrained optimization with offspring repair based gravitational search algorithm. In Evolutionary Computation (CEC), 2013 IEEE Congress on, pages 2414--2421, 2013.
[15]
H. Richter. Detecting change in dynamic fitness landscapes. In Evolutionary Computation, 2009. CEC '09. IEEE Congress on, pages 1613--1620, 2009.
[16]
Y. Shengxiang. Memory-based immigrants for genetic algorithms in dynamic environments. In Proceedings of the 2005 conference on Genetic and evolutionary computation, GECCO '05, pages 1115--1122, New York, NY, USA, 2005. ACM.

Cited By

View all
  • (2023)Evolutionary approach for dynamic constrained optimization problemsAlexandria Engineering Journal10.1016/j.aej.2022.10.07266(827-843)Online publication date: Mar-2023
  • (2022)Solving constrained problems with dynamic objective functions2022 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC55065.2022.9870354(1-8)Online publication date: 18-Jul-2022
  • (2020)Sensitivity-Based Change Detection for Dynamic Constrained OptimizationIEEE Access10.1109/ACCESS.2020.29991618(103900-103912)Online publication date: 2020
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
GECCO Companion '15: Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation
July 2015
1568 pages
ISBN:9781450334884
DOI:10.1145/2739482
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: 11 July 2015

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. constraint-handling
  2. differential evolution
  3. dynamic optimization

Qualifiers

  • Short-paper

Funding Sources

Conference

GECCO '15
Sponsor:

Acceptance Rates

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)0
  • Downloads (Last 6 weeks)0
Reflects downloads up to 16 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2023)Evolutionary approach for dynamic constrained optimization problemsAlexandria Engineering Journal10.1016/j.aej.2022.10.07266(827-843)Online publication date: Mar-2023
  • (2022)Solving constrained problems with dynamic objective functions2022 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC55065.2022.9870354(1-8)Online publication date: 18-Jul-2022
  • (2020)Sensitivity-Based Change Detection for Dynamic Constrained OptimizationIEEE Access10.1109/ACCESS.2020.29991618(103900-103912)Online publication date: 2020
  • (2018)Dynamic differential evolution with combined variants and a repair method to solve dynamic constrained optimization problemsSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-016-2353-122:2(541-570)Online publication date: 1-Jan-2018

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