research-article

Integrating user preferences with particle swarms for multi-objective optimization

Authors:

Upali K. Wickramasinghe,

Xiaodong LiAuthors Info & Claims

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

Pages 745 - 752

https://doi.org/10.1145/1389095.1389237

Published: 12 July 2008 Publication History

Abstract

This paper proposes a method to use reference points as preferences to guide a particle swarm algorithm to search towards preferred regions of the Pareto front. A decision maker can provide several reference points, specify the extent of the spread of solutions on the Pareto front as desired, or include any bias between the objectives as preferences within a single execution. We incorporate the reference point method into two multi-objective particle swarm algorithms, the non-dominated sorting PSO, and the maximinPSO. This paper first demonstrates the usefulness of the proposed reference point based particle swarm algorithms, then compare the two algorithms using a hyper-volume metric. Both particle swarm algorithms are able to converge to the preferred regions of the Pareto front using several feasible or infeasible reference points.

References

[1]

R. Balling. The maximin fitness function; multi-objective city and regional planning. In Evolutionary Multiobjective Optimization (EMO), volume 2632 of Lecture Notes in Computer Science, pages 1--15. Springer, 2003.]]

Digital Library

[2]

J. Branke, T. Kausler, and H. Schmeck. Guidance in Evolutionary Multi-Objective Optimization. Advances in Engineering Software, 32:499--507, 2001.]]

[3]

M. Clerc and J. Kennedy. The particle swarm - explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation, 6(1):58--73, 2002.]]

Digital Library

[4]

K. Deb. Solving goal programming problems using multi-objective genetic algorithms. In Congress on Evolutionary Computation (CEC), 1999.]]

[5]

K. Deb. Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley & Sons, Inc., New York, NY, USA, 2001.]]

Digital Library

[6]

K. Deb, S. Agrawal, A. Pratab, and T. Meyarivan. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2):182--197, 2002.]]

Digital Library

[7]

K. Deb and A. Kumar. Interactive evolutionary multi-objective optimization and decision-making using reference direction method. In Genetic and Evolutionary Computation Conference (GECCO), pages 781--788, New York, NY, USA, 2007. ACM Press.]]

Digital Library

[8]

K. Deb and A. Kumar. Light beam search based multi-objective optimization using evolutionary algorithms. In Congress on Evolutionary Computation (CEC), 2007.]]

[9]

K. Deb and J. Sundar. Reference point based multi-objective optimization using evolutionary algorithms. In Genetic and Evolutionary Computation Conference (GECCO), pages 635--642, New York, NY, USA, 2006. ACM Press.]]

Digital Library

[10]

K. Deb, L. Thiele, M. Laumanns, and E. Zitzler. Scalable test problems for evolutionary multi-objective optimization. In Evolutionary Multiobjective Optimization (EMO): Theoretical Advances and Applications, pages 105--145. Springer, 2005.]]

[11]

M. Ehrgott and X. Gandibleux. Multiple Criteria Optimization: State of the Art Annotated Bibliographic Survey, volume52 of International Series in Operations Research and Management Science. Kluwer Academic Publishers, Boston, 2002. 496 pages. ISBN 1-4020-7128-0.]]

[12]

M. Emmerich, N. Beume, and B. Naujoks. An emo algorithm using the hypervolume measure as selection criterion. In Evolutionary Multi-Criterion Optimization (EMO), LNCS, vol. 3410, pages 62--76, Berlin, 2005. Springer.]]

Digital Library

[13]

A. Jaszkiewicz and R.Slowinski. The light beam search approach -an overview of methodology and applications. European Journal of Operational Research, 113(2):300--314, 1999.]]

[14]

J. Kennedy and R. C. Eberhart. Swarm intelligence. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 2001.]]

Digital Library

[15]

J. Knowles and D. Corne. On metrics for comparing on-dominated sets. In Congress on Evolutionary Computation (CEC), 2002.]]

[16]

X.Li. A non-dominated sorting particle swarm optimizer for multiobjective optimization. In Genetic and Evolutionary Computation Conference (GECCO), volume 2723 of Lecture Notes in Computer Science, pages 37--48. Springer, 2003.]]

Digital Library

[17]

X. Li. Better spread and convergence: Particle swarm multiobjective optimization using the maximin fitness function. In Genetic and Evolutionary Computation Conference (GECCO), volume 3102 of Lecture Notes in Computer Science, pages 117--128. Springer, 2004.]]

[18]

A. Messac and C. A. Mattson. Normal constraint method with guarantee of even representation of complete pareto frontier. American Institute of Aeronautics and Astronautic (AIAA)Journal, 2004.]]

[19]

K. Miettinen. Some methods for nonlinear multi-objective optimization. In Evolutionary Multi-Criterion Optimization, volume 1993 of Lecture Notes in Computer Science, pages 1--20. Springer, 2001.]]

Digital Library

[20]

M. J. Osborne and A. Rubinstein. A Course in Game Theory. The MIT Press, July 1994.]]

[21]

M. Reyes-Sierra and C.A. Coello Coello. Multi-Objective Particle Swarm Optimizers: A Survey of the State-of-the-Art. International Journal of Computational Intelligence Research, 2(3):287--308, 2006.]]

[22]

L. Thiele, K. Miettinen, P. Korhonen, and J. Molina. A preference-based interactive evolutionary algorithm for multiobjective optimization. In Helsinki School of Economics Technical Report Number W-412, 2007.]]

[23]

E. Zitzler, K. Deb, and L. Thiele. Comparison of multiobjective evolutionary algorithms: Empirical results. Evolutionary Computation, 8(2):173--195, 2000.]]

Digital Library

Cited By

Tang RXiao HChen XHuang Y(2024)A Hybrid Optimization Method Based on Simplified Mechanical Model for High-Stress MagnetsIEEE Transactions on Magnetics10.1109/TMAG.2023.332941460:1(1-11)Online publication date: Jan-2024
https://doi.org/10.1109/TMAG.2023.3329414
Li KLiao MDeb KMin GYao X(2020)Does Preference Always Help? A Holistic Study on Preference-Based Evolutionary Multiobjective Optimization Using Reference PointsIEEE Transactions on Evolutionary Computation10.1109/TEVC.2020.298755924:6(1078-1096)Online publication date: Dec-2020
https://doi.org/10.1109/TEVC.2020.2987559
Jamwal PAbdikenov BHussain S(2019)Evolutionary Optimization Using Equitable Fuzzy Sorting Genetic Algorithm (EFSGA)IEEE Access10.1109/ACCESS.2018.28902747(8111-8126)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2018.2890274
Show More Cited By

Index Terms

Integrating user preferences with particle swarms for multi-objective optimization
1. Computing methodologies
  1. Artificial intelligence
    1. Control methods
    2. Search methodologies
2. Theory of computation
  1. Design and analysis of algorithms
    1. Algorithm design techniques
      1. Dynamic programming

Recommendations

Using a distance metric to guide PSO algorithms for many-objective optimization
GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation

In this paper we propose to use a distance metric based on user-preferences to efficiently find solutions for many-objective problems. We use a particle swarm optimization (PSO) algorithm as a baseline to demonstrate the usefulness of this distance ...
Enhanced multi-objective particle swarm optimisation for estimating hand postures
Abstract
Multi-objective problems with conflicting objectives cannot be effectively solved by aggregation-based methods. The answer to such problems is a Pareto optimal solution set. Due to the difficulty of solving multi-objective problems ...
Many Objective Particle Swarm Optimization

Many-objective problems refer to the optimization problems containing more than three conflicting objectives. To obtain a representative set of well-distributed non-dominated solutions close to Pareto front in the objective space remains a challenging ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

July 2008

1814 pages

ISBN:9781605581309

DOI:10.1145/1389095

Conference Chair:
Conor Ryan
University of Limerick, Ireland
,
Editor:
Maarten Keijzer
Chordiant Software International B.V., The Netherlands

Copyright © 2008 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

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 July 2008

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

GECCO08

Sponsor:

GECCO08: Genetic and Evolutionary Computation Conference

July 12 - 16, 2008

GA, Atlanta, USA

Acceptance Rates

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

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

28
Total Citations
View Citations
339
Total Downloads

Downloads (Last 12 months)4
Downloads (Last 6 weeks)1

Reflects downloads up to 08 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Tang RXiao HChen XHuang Y(2024)A Hybrid Optimization Method Based on Simplified Mechanical Model for High-Stress MagnetsIEEE Transactions on Magnetics10.1109/TMAG.2023.332941460:1(1-11)Online publication date: Jan-2024
https://doi.org/10.1109/TMAG.2023.3329414
Li KLiao MDeb KMin GYao X(2020)Does Preference Always Help? A Holistic Study on Preference-Based Evolutionary Multiobjective Optimization Using Reference PointsIEEE Transactions on Evolutionary Computation10.1109/TEVC.2020.298755924:6(1078-1096)Online publication date: Dec-2020
https://doi.org/10.1109/TEVC.2020.2987559
Jamwal PAbdikenov BHussain S(2019)Evolutionary Optimization Using Equitable Fuzzy Sorting Genetic Algorithm (EFSGA)IEEE Access10.1109/ACCESS.2018.28902747(8111-8126)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2018.2890274
Li KChen RMin GYao X(2018)Integration of Preferences in Decomposition Multiobjective OptimizationIEEE Transactions on Cybernetics10.1109/TCYB.2018.285936348:12(3359-3370)Online publication date: Dec-2018
https://doi.org/10.1109/TCYB.2018.2859363
Xin BChen LChen JIshibuchi HHirota KLiu B(2018)Interactive Multiobjective Optimization: A Review of the State-of-the-ArtIEEE Access10.1109/ACCESS.2018.28568326(41256-41279)Online publication date: 2018
https://doi.org/10.1109/ACCESS.2018.2856832
Candon MCarrese RJoseph NOgawa HMarzocca P(2016)Evolutionary optimization of transonic airfoils for static and dynamic trim performanceJournal of Intelligent Material Systems and Structures10.1177/1045389X1667901928:8(1071-1088)Online publication date: 6-Dec-2016
https://doi.org/10.1177/1045389X16679019
Narukawa KSetoguchi YTanigaki YOlhofer MSendhoff BIshibuchi H(2016)Preference representation using Gaussian functions on a hyperplane in evolutionary multi-objective optimizationSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-015-1674-920:7(2733-2757)Online publication date: 1-Jul-2016
https://dl.acm.org/doi/10.1007/s00500-015-1674-9
Hiwa SNishioka MHiroyasu TMiki M(2015)Novel search scheme for multi-objective evolutionary algorithms to obtain well-approximated and widely spread Pareto solutionsSwarm and Evolutionary Computation10.1016/j.swevo.2015.01.00422(30-46)Online publication date: Jun-2015
https://doi.org/10.1016/j.swevo.2015.01.004
Mohammadi AOmidvar MLi XDeb K(2014)Integrating user preferences and decomposition methods for many-objective optimization2014 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2014.6900595(421-428)Online publication date: Jul-2014
https://doi.org/10.1109/CEC.2014.6900595
Purshouse RDeb KMansor MMostaghim SWang R(2014)A review of hybrid evolutionary multiple criteria decision making methods2014 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2014.6900368(1147-1154)Online publication date: Jul-2014
https://doi.org/10.1109/CEC.2014.6900368
Show More Cited By

View Options

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.

Figures

Tables

Media

View Table of Conten