research-article

Reference point based multi-objective evolutionary algorithms for group decisions

Authors:

Jella Pfeiffer,

Franz RothlaufAuthors Info & Claims

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

Pages 697 - 704

https://doi.org/10.1145/1389095.1389231

Published: 12 July 2008 Publication History

Abstract

While in the past decades research on multi-objective evolutionary algorithms (MOEA) has aimed at finding the whole set of Pareto optimal solutions, current approaches focus on only those parts of the Pareto front which satisfy the preferences of the decision maker (DM). Therefore, they integrate the DM early on in the optimization process instead of leaving him/her alone with the final choice of one solution among the whole Pareto optimal set. In this paper, we address an aspect which has been neglected so far in the research on integrating preferences: in most real-world problems, there is not only one DM, but a group of DMs trying to find one consensus decision all participants are willed to agree to. Therefore, our aim is to introduce methods which focus on the part of the Pareto front which satisfies the preferences of several DMs concurrently. We assume that the DMs have some vague notion of their preferences a priori the search in form of a reference point or goal. Thus, we present and compare several reference point based approaches for group decisions and evaluate them on three ZDT and two flow shop problems.

References

[1]

K. J. Arrow. Social Choice and Individual Values. John Wiley, New York, 1951.]]

[2]

T. Bagchi. Pareto-optimal solutions for multi-objective production scheduling problems. In Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization, volume 1993 of Lecture Notes in Computer Science, pages 458--471. Springer-Verlag, 2001.]]

Digital Library

[3]

J. Branke and K. Deb. Integrating user preferences into evolutionary multi-objective optimization. In Y. Jin, editor, Knowledge Incorporation in Evolutionary Computation under the book series on Studies in Fuzziness and Soft Computing, volume 167, pages 461--477. Springer-Verlag, 2004.]]

[4]

J. Branke, T. Kauβler, and H. Schmeck. Guidance in evolutionary multi-objective optimization. Advances in Engineering Software, 32:499--507, 2001.]]

[5]

C. Coello. Handling preferences in evolutionary multiobjective optimization: A survey. In Proceedings of the Congress on Evolutionary Computation (CEC-00), volume 1, pages 30--37, 2000.]]

[6]

C. Coello, G. Lamont, and D. Van Veldhuizen. Evolutionary Algorithms for Solving Multi-Objective Problems. Springer, New York, 2007.]]

Digital Library

[7]

D. Cvetkovic and I. C. Parmee. Preferences and their application in evolutionary multiobjective optimization. IEEE Transactions on Evolutionary Computation, 6(1):42--57, 2002.]]

Digital Library

[8]

K. Deb. Solving goal programming problems using multi-objective genetic algorithms. In Proceedings of the Congress on Evolutionary Computation (CEC 99), pages 77--84, 1999.]]

[9]

K. Deb. Multi-objective evolutionary algorithms: Introducing bias among pareto-optimal solutions. In A. Ghosh and S. Tsutsui, editors, Advances in Evolutionary Computing: Theory and Applications, Chapter 10, pages 263--292. Springer-Verlag, London, 2003.]]

Digital Library

[10]

K. Deb and A. Kumar. Interactive evolutionary multi-objective optimization and decision-making using reference direction method. In Proceedings of the Conference on Genetic and Evolutionary Computation (GECCO-07), pages 781--788. ACM, 2007.]]

Digital Library

[11]

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

Digital Library

[12]

K. Deb and J. Sundar. Reference point based multi-objective optimization using evolutionary algorithms. In Proceedings of the Conference on Genetic and Evolutionary Computation (GECCO-06), pages 635--642. ACM, 2006.]]

Digital Library

[13]

E. Demirkol, S. Mehta, and R. Uzsoy. Benchmarks for shop scheduling problems. European Journal of Operational Research, 109(1):137--141, August 1998.]]

[14]

C. M. Fonseca and P. J. Fleming. Genetic algorithms for multiobjective optimization: Formulation, discussion and generalization. In Proceedings of the Fifth International Conference on Genetic Algorithms, pages 416--423. Morgan Kaufmann, 1993.]]

Digital Library

[15]

M. Köksalan, M. Karwan, and S. Zionts. An improved method for solving multiple criteria problems involving discrete alternatives. IEEE Transactions on Systems, Man, and Cybernetics, 14:24--34, 1984.]]

[16]

S. P. Phelps and M. Köksalan. An interactive evolutionary metaheuristic for multiobjective combinatorial optimization. Management Science, 49(12):1726--1738, 2003.]]

Digital Library

[17]

A. Sen. The impossibility of a paretian liberal. Journal of Political Economy, 78:152--157, 1970.]]

[18]

E. Taillard. Benchmarks for basic scheduling problems. European Journal of Operational Research, 64(2):278--285, 1993.]]

[19]

E. G. Talbi, M. Rahoual, M. H. Mabed, and C. Dhaenens. A hybrid evolutionary approach for multicriteria optimization problems: Application to the flow shop. In E. Zitzler, K. Deb, L. Thiele, C. A. C. Coello, and D. Corne, editors, Lecture Notes in Computer Science, pages 416--428. Springer, Berlin, 2001.]]

Digital Library

[20]

K. C. Tan, T. H. Lee, and E. F. Khor. Evolutionary algorithms with goal and priority information for multi-objective optimization. In Proceedings of the Congress on Evolutionary Computation (CEC-99), pages 106--113, 1999.]]

[21]

M. Tanaka and T. Tanino. Global optimization by the genetic algorithm in a multiobjective decision support system. In Proceedings of the 10th International conference on Multiple Criteria Decision Making, volume 2, pages 261--270, 1992.]]

[22]

T. Tanino, M. Tanaka, and C. Hojo. An interactive multicriteria decision making method by using a genetic algorithm. In Proceedings of the 2nd International Conference on Systems Science and Systems Engineering, pages 381--386, 1993.]]

[23]

L. Thiele, K. Miettinen, P. Korhonen, and J. Molina. A preference-based interactive evolutionary algorithm for multiobjective optimization. Technical report, 2007.]]

[24]

V. T'kindt and J. C. Billaut. Multicriteria Scheduling: Theory, Models and Algorithms. Springer, Berlin, 2002.]]

Digital Library

[25]

D. S. Todd and P. Sen. Directed multiple objective search of design spaces using genetic algorithms and neural networks. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-99), volume 2, pages 1738--1743, 1999.]]

[26]

E. Turban. Decision Support Systems and Expert Systems: Managerial Perspectives. MacMillan, 1988.]]

Digital Library

[27]

D. A. V. Veldhuizen and G. B. Lamont. Multiobjective evolutionary algorithms: Analysing the state-of-the-art. IEEE Transactions on Evolutionary Computation, 8(2):125--147, 2000.]]

Digital Library

[28]

A. Wierzbicki. The use of reference objectives in multiobjective optimization. In G. Fandel and T. Gal, editors, Lectures Notes in Economics and Mathematical Systems, volume 177, pages 468--486. Springer, New York, 1980.]]

[29]

J. M. Yeh, C. Lin, B. Kreng, and J. Y. Gee. A modified procedure for synthesising ratio judgements in the analytic hierarchy process. Journal of the Operational Research Society, 50(8):867--873, 1999.]]

[30]

S. Zahir. Clusters in a group: Decision making in the vector space formulation of the analytic hierarchy process. European Journal of Operations Research, 112(3):620--634, 1999.]]

[31]

E. Zitzler, K. Deb, and L. Thiele. Comparison of Multiobjective Evolutionary Algorithms: Empirical Results. Evolutionary Computation, 8(2):173--195, 2000.]]

Digital Library

[32]

E. Zitzler and S. Künzli. Indicator-Based Selection in Multiobjective Search. In Conference on Parallel Problem Solving from Nature (PPSN VIII), pages 832--842. Springer, 2004.]]

Cited By

Pajasmaa JMiettinen KSilvennoinen J(2024)Group Decision Making in Multiobjective Optimization: A Systematic Literature ReviewGroup Decision and Negotiation10.1007/s10726-024-09915-8Online publication date: 18-Dec-2024
https://doi.org/10.1007/s10726-024-09915-8
Cinalli DMartí LSanchez-Pi NGarcia AWainwright RCorchado JBechini AHong J(2015)Collective preferences in evolutionary multi-objective optimizationProceedings of the 30th Annual ACM Symposium on Applied Computing10.1145/2695664.2695926(133-138)Online publication date: 13-Apr-2015
https://dl.acm.org/doi/10.1145/2695664.2695926
Bilbao-Terol AJiménez MArenas-Parra M(2014)A group decision making model based on goal programming with fuzzy hierarchy: an application to regional forest planningAnnals of Operations Research10.1007/s10479-014-1633-3245:1-2(137-162)Online publication date: 25-May-2014
https://doi.org/10.1007/s10479-014-1633-3
Show More Cited By

Index Terms

Reference point based multi-objective evolutionary algorithms for group decisions
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

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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]

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Publication History

Published: 12 July 2008

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GECCO08: Genetic and Evolutionary Computation Conference

July 12 - 16, 2008

GA, Atlanta, USA

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

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Cited By

Pajasmaa JMiettinen KSilvennoinen J(2024)Group Decision Making in Multiobjective Optimization: A Systematic Literature ReviewGroup Decision and Negotiation10.1007/s10726-024-09915-8Online publication date: 18-Dec-2024
https://doi.org/10.1007/s10726-024-09915-8
Cinalli DMartí LSanchez-Pi NGarcia AWainwright RCorchado JBechini AHong J(2015)Collective preferences in evolutionary multi-objective optimizationProceedings of the 30th Annual ACM Symposium on Applied Computing10.1145/2695664.2695926(133-138)Online publication date: 13-Apr-2015
https://dl.acm.org/doi/10.1145/2695664.2695926
Bilbao-Terol AJiménez MArenas-Parra M(2014)A group decision making model based on goal programming with fuzzy hierarchy: an application to regional forest planningAnnals of Operations Research10.1007/s10479-014-1633-3245:1-2(137-162)Online publication date: 25-May-2014
https://doi.org/10.1007/s10479-014-1633-3
Liu GWu GZheng TLing Q(2011)Integrating preference based weighted sum into evolutionary multi-objective optimization2011 Seventh International Conference on Natural Computation10.1109/ICNC.2011.6022362(1251-1255)Online publication date: Jul-2011
https://doi.org/10.1109/ICNC.2011.6022362
Bechikh SBen Said LGhedira K(2011)Negotiating decision makers' reference points for group preference-based Evolutionary Multi-objective Optimization2011 11th International Conference on Hybrid Intelligent Systems (HIS)10.1109/HIS.2011.6122135(377-382)Online publication date: Dec-2011
https://doi.org/10.1109/HIS.2011.6122135

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