skip to main content
10.1145/3205651.3205653acmconferencesArticle/Chapter ViewAbstractPublication PagesgeccoConference Proceedingsconference-collections
poster

Dynamic constrained multi-objective evolutionary algorithms with a novel selection strategy for constrained optimization

Published: 06 July 2018 Publication History

Abstract

The recently proposed dynamic constrained multi-objective evolutionary algorithm (DCMOEA) is effective to handle constrained optimization problems (COPs). However, one drawback of DCMOEA is it mainly searches the global optimum from infeasible regions, which may result in the bias against feasible solutions. Then the useful information about the optimal direction of feasible regions is not fully used. To overcome this defect, this paper proposes a novel selection strategy based on DCMOEA framework, called NSDCMOEA to solve COPs. The performance of NSDCMOEA is evaluated using a set of benchmark suites. Experimental results validate that the proposed method is better than or very competitive to five state-of-the-art algorithms.

References

[1]
K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan. 2002. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6, 2 (2002), 182--197.
[2]
W. Gong, Z. Cai, and D. Liang. 2015. Adaptive Ranking Mutation Operator Based Differential Evolution for Constrained Optimization. IEEE Trans. Cybern. 45, 4 (2015), 716--727.
[3]
R. Jiao, S. Zeng, J. S. Alkasassbeh, and C. Li. 2017. Dynamic multi-objective evolutionary algorithms for single-objective optimization. Appl. Soft Comput. J. 61 (2017), 793--805.
[4]
R. Mallipeddi and P. N. Suganthan. 2010. Problem definitions and evaluation criteria for the CEC 2010 Competition on Constrained Real-Parameter Optimization. Nanyang Technological University (2010).
[5]
T. Takahama and S. Sakai. 2010. Efficient constrained optimization by the ε constrained adaptive differential evolution. In Evolutionary Computation (CEC), IEEE Congress on. IEEE, 1--8.
[6]
Y. Wang and Z. Cai. 2012. Combining Multiobjective Optimization With Differential Evolution to Solve Constrained Optimization Problems. IEEE Trans. Evol. Comput. 16, 1 (2012), 117--134.
[7]
S. Zeng, R. Jiao, C. Li, X. Li, and J. S. Alkasassbeh. 2017. A General Framework of Dynamic Constrained Multiobjective Evolutionary Algorithms for Constrained Optimization. IEEE Trans. Cybern. 47, 9 (2017), 2678--2688.
[8]
S. Zeng, R. Jiao, C. Li, and R. Wang. 2017. Constrained optimisation by solving equivalent dynamic loosely-constrained multiobjective optimisation problem. Int. J. Bio-Inspired Computation (2017), to be published.
[9]
W. Zhang, G.G Yen, and Z. He. 2014. Constrained Optimization Via Artificial Immune System. IEEE Trans. Cybern. 44, 2 (2014), 185--198.

Cited By

View all
  • (2022)A dynamic resource allocation strategy for collaborative constrained multi-objective optimization algorithmApplied Intelligence10.1007/s10489-022-03820-w53:9(10176-10201)Online publication date: 16-Aug-2022
  • (2019)Evolutionary Constrained Multi-objective Optimization using NSGA-II with Dynamic Constraint Handling2019 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2019.8790172(1634-1641)Online publication date: Jun-2019
  1. Dynamic constrained multi-objective evolutionary algorithms with a novel selection strategy for constrained optimization

      Recommendations

      Comments

      Information & Contributors

      Information

      Published In

      cover image ACM Conferences
      GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference Companion
      July 2018
      1968 pages
      ISBN:9781450357647
      DOI:10.1145/3205651
      Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

      Sponsors

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 06 July 2018

      Check for updates

      Author Tags

      1. constrained optimization
      2. constraint-handling
      3. multi-objective

      Qualifiers

      • Poster

      Funding Sources

      Conference

      GECCO '18
      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)1
      • Downloads (Last 6 weeks)0
      Reflects downloads up to 20 Feb 2025

      Other Metrics

      Citations

      Cited By

      View all
      • (2022)A dynamic resource allocation strategy for collaborative constrained multi-objective optimization algorithmApplied Intelligence10.1007/s10489-022-03820-w53:9(10176-10201)Online publication date: 16-Aug-2022
      • (2019)Evolutionary Constrained Multi-objective Optimization using NSGA-II with Dynamic Constraint Handling2019 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2019.8790172(1634-1641)Online publication date: Jun-2019

      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