technical-note

Epsilon constrained method for constrained multiobjective optimization problems: some preliminary results

Authors:

Zhun FanAuthors Info & Claims

GECCO Comp '14: Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation

Pages 1181 - 1186

https://doi.org/10.1145/2598394.2610012

Published: 12 July 2014 Publication History

Abstract

In this paper, the ε constrained method and Adaptive operator selection (AOS) are used in Multiobjective evolutionary algorithm based on decomposition (MOEA/D). The ε constrained method is an algorithm transformation method, which can convert algorithms for unconstrained problems to algorithms for constrained problems using the ε level comparison, which compares search points based on the pair of objective value and constraint violation of them. AOS is used to determine the application rates of different operators in an online manner based on their recent performances within an optimization process. The experimental results show our proposed approach for multiobjective constrained optimization is very competitive compared with other state-of-art algorithms.

References

[1]

Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2009, Trondheim, Norway, 18-21 May, 2009. IEEE, 2009.

[2]

C. A. Coello Coello. Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Computer methods in applied mechanics and engineering, 191(11):1245--1287, 2002.

[3]

Á. Fialho. Adaptive operator selection for optimization. PhD, Ecole Doctorale d'Informatique, Universite Paris-Sud, Paris, 2010.

[4]

M. A. Jan and Q. Zhang. Eee moea/d for constrained multiobjective optimization: Some preliminary experimental results. UKCI, 2010.

[5]

K. Li, Á. Fialho, S. Kwong, and Q. Zhang. Adaptive operator selection with bandits for a multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evolutionary Computation, 18(1):114--130, 2014.

[6]

H. lin Liu and X. Li. The multiobjective evolutionary algorithm based on determined weight and sub-regional search. In IEEE Congress on Evolutionary Computation {1}, pages 1928--1934.

Digital Library

[7]

M. Liu, X. Zou, Y. Chen, and Z. Wu. Performance assessment of dmoea-dd with cec 2009 moea competition test instances. In IEEE Congress on Evolutionary Computation {1}, pages 2913--2918.

Digital Library

[8]

T. Takahama and S. Sakai. Constrained optimization by applying the α constrained method to the nonlinear simplex method with mutations. Evolutionary Computation, IEEE Transactions on, 9(5):437--451, 2005.

Digital Library

[9]

T. Takahama and S. Sakai. Efficient constrained optimization by the constrained differential evolution with rough approximation using kernel regression. In IEEE Congress on Evolutionary Computation, pages 1334--1341. IEEE, 2013.

[10]

L.-Y. Tseng and C. Chen. Multiple trajectory search for unconstrained/constrained multi-objective optimization. In IEEE Congress on Evolutionary Computation {1}, pages 1951--1958.

Digital Library

[11]

J. Yao, Q. Zhang, and J. Lei. Recent developments in natural computation. Neurocomputing, 72(13-15):2833--2834, 2009.

Digital Library

[12]

Q. Zhang et al. Moea/d for constrained multiobjective optimization: Some preliminary experimental results. In 2010 UK Workshop on Computational Intelligence (UKCI), pages 1--6, 2010.

[13]

Q. Zhang and H. Li. Moea/d: A multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evolutionary Computation, 11(6):712--731, 2007.

Digital Library

[14]

Q. Zhang, W. Liu, and H. Li. The performance of a new version of moea/d on cec09 unconstrained mop test instances. In IEEE Congress on Evolutionary Computation {1}, pages 203--208.

Digital Library

[15]

Q. Zhang, W. Liu, and H. Li. The performance of a new version of moea/d on cec09 unconstrained mop test instances. In IEEE Congress on Evolutionary Computation {1}, pages 203--208.

Digital Library

[16]

Q. Zhang, A. Zhou, S. Zhao, P. N. Suganthan, W. Liu, and S. Tiwari. Multiobjective optimization test instances for the cec 2009 special session and competition. University of Essex, Colchester, UK and Nanyang Technological University, Singapore, Special Session on Performance Assessment of Multi-Objective Optimization Algorithms, Technical Report, 2008.

Cited By

Yonekura KYamada ROgawa SSuzuki K(2024)Hypervolume-Based Multi-Objective Optimization Method Applying Deep Reinforcement Learning to the Optimization of Turbine Blade ShapeAI10.3390/ai50400855:4(1731-1742)Online publication date: 24-Sep-2024
https://doi.org/10.3390/ai5040085
Wang BQin YMeng XWang YLiu Z(2024)ATM-R: An Adaptive Tradeoff Model With Reference Points for Constrained Multiobjective Evolutionary OptimizationIEEE Transactions on Cybernetics10.1109/TCYB.2023.332994754:8(4475-4488)Online publication date: Aug-2024
https://doi.org/10.1109/TCYB.2023.3329947
Yu KWang LLiang JWang HQiao KLiang T(2024)Constraint subsets-based evolutionary multitasking for constrained multiobjective optimizationSwarm and Evolutionary Computation10.1016/j.swevo.2024.10153186(101531)Online publication date: Apr-2024
https://doi.org/10.1016/j.swevo.2024.101531
Show More Cited By

Index Terms

Epsilon constrained method for constrained multiobjective optimization problems: some preliminary results
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Heuristic function construction

Recommendations

Constrained multiobjective distance minimization problems
GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference

Various distance minimization problems (DMPs) have been proposed to visualize the search behaviors of evolutionary multiobjective optimization (EMO) algorithms in solving many-objective problems, multiobjective multimodal problems, and dynamic ...
Multi-objective optimization based reverse strategy with differential evolution algorithm for constrained optimization problems

We rebuilt the model of constrained optimization problems, called reversed model.We developed a comparison strategy based on origin and new model.Difference between usual algorithm and proposed one is discussed.Experimental results show the ...
Constrained optimisation by solving equivalent dynamic loosely-constrained multiobjective optimisation problem

A constrained optimisation problem COP is solved by solving an equivalent dynamic loosely-constrained multiobjective optimisation problem in this paper. Two strategies are considered. 1 An additional objective constrained-violation objective is ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

GECCO Comp '14: Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation

July 2014

1524 pages

ISBN:9781450328814

DOI:10.1145/2598394

Editor-in-chief:
Christian Igel
Ruhr University of Bochum, University of Copenhagen
,
General Chair:
Dirk V. Arnold
Dalhousie University

Copyright © 2014 ACM.

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

SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 July 2014

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Technical-note

Conference

GECCO '14

Sponsor:

SIGEVO

GECCO '14: Genetic and Evolutionary Computation Conference

July 12 - 16, 2014

BC, Vancouver, Canada

Acceptance Rates

GECCO Comp '14 Paper Acceptance Rate 180 of 544 submissions, 33%;

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

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

36
Total Citations
View Citations
1,372
Total Downloads

Downloads (Last 12 months)157
Downloads (Last 6 weeks)7

Reflects downloads up to 24 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

Yonekura KYamada ROgawa SSuzuki K(2024)Hypervolume-Based Multi-Objective Optimization Method Applying Deep Reinforcement Learning to the Optimization of Turbine Blade ShapeAI10.3390/ai50400855:4(1731-1742)Online publication date: 24-Sep-2024
https://doi.org/10.3390/ai5040085
Wang BQin YMeng XWang YLiu Z(2024)ATM-R: An Adaptive Tradeoff Model With Reference Points for Constrained Multiobjective Evolutionary OptimizationIEEE Transactions on Cybernetics10.1109/TCYB.2023.332994754:8(4475-4488)Online publication date: Aug-2024
https://doi.org/10.1109/TCYB.2023.3329947
Yu KWang LLiang JWang HQiao KLiang T(2024)Constraint subsets-based evolutionary multitasking for constrained multiobjective optimizationSwarm and Evolutionary Computation10.1016/j.swevo.2024.10153186(101531)Online publication date: Apr-2024
https://doi.org/10.1016/j.swevo.2024.101531
Yuan JYang SYan W(2024)An adaptive uniform search framework for constrained multi-objective optimizationApplied Soft Computing10.1016/j.asoc.2024.111800162(111800)Online publication date: Sep-2024
https://doi.org/10.1016/j.asoc.2024.111800
Liang JLin HYue CBan XYu K(2024)Evolutionary constrained multi-objective optimization: a reviewVicinagearth10.1007/s44336-024-00006-51:1Online publication date: 4-Jul-2024
https://doi.org/10.1007/s44336-024-00006-5
Banerjee APradhan SMisra BChakraborty S(2024)A Guide to Meta-Heuristic Algorithms for Multi-objective Optimization: Concepts and ApproachesApplied Multi-objective Optimization10.1007/978-981-97-0353-1_1(1-19)Online publication date: 17-Mar-2024
https://doi.org/10.1007/978-981-97-0353-1_1
de Veluz MRedi AMaaliw RPersada SPrasetyo YYoung M(2023)Scenario-Based Multi-Objective Location-Routing Model for Pre-Disaster Planning: A Philippine Case StudySustainability10.3390/su1506488215:6(4882)Online publication date: 9-Mar-2023
https://doi.org/10.3390/su15064882
Yasuda YTamura KYasuda KSilva SPaquete L(2023)Extending MOEA/D to Constrained Multi-objective Optimization via Making Constraints an Objective FunctionProceedings of the Companion Conference on Genetic and Evolutionary Computation10.1145/3583133.3590583(435-438)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583133.3590583
Liang JLin HYue CYu KGuo YQiao K(2023)Multiobjective Differential Evolution With Speciation for Constrained Multimodal Multiobjective OptimizationIEEE Transactions on Evolutionary Computation10.1109/TEVC.2022.319425327:4(1115-1129)Online publication date: Aug-2023
https://doi.org/10.1109/TEVC.2022.3194253
Liang JBan XYu KQu BQiao KYue CChen KTan K(2023)A Survey on Evolutionary Constrained Multiobjective OptimizationIEEE Transactions on Evolutionary Computation10.1109/TEVC.2022.315553327:2(201-221)Online publication date: Apr-2023
https://doi.org/10.1109/TEVC.2022.3155533
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents