Article

Incorporation of decision maker's preference into evolutionary multiobjective optimization algorithms

Authors:

Hisao Ishibuchi,

Yusuke Nojima,

Kaname Narukawa,

Tsutomu DoiAuthors Info & Claims

GECCO '06: Proceedings of the 8th annual conference on Genetic and evolutionary computation

Pages 741 - 742

https://doi.org/10.1145/1143997.1144126

Published: 08 July 2006 Publication History

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Abstract

The main characteristic feature of evolutionary multiobjective optimization (EMO) is that no a priori information about the decision maker's preference is utilized in the search phase. EMO algorithms try to find a set of well-distributed Pareto-optimal solutions with a wide range of objective values. It is, however, very difficult for EMO algorithms to find a good solution set of a multiobjective combinatorial optimization problem with many decision variables and/or many objectives. In this paper, we propose an idea of incorporating the decision maker's preference into EMO algorithms to efficiently search for Pareto-optimal solutions of such a hard multiobjective optimization problem.

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Cited By

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Méndez MFrutos MMiguel FAguasca-Colomo R(2020)TOPSIS Decision on Approximate Pareto Fronts by Using Evolutionary Algorithms: Application to an Engineering Design ProblemMathematics10.3390/math81120728:11(2072)Online publication date: 20-Nov-2020
https://doi.org/10.3390/math8112072
Cheng RRodemann TFischer MOlhofer MJin Y(2017)Evolutionary Many-Objective Optimization of Hybrid Electric Vehicle Control: From General Optimization to Preference ArticulationIEEE Transactions on Emerging Topics in Computational Intelligence10.1109/TETCI.2017.26691041:2(97-111)Online publication date: Apr-2017
https://doi.org/10.1109/TETCI.2017.2669104
Aissanou FPetrowski A(2013)Autonomous multi-criteria decision making for route selection in a telecommunication network2013 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making (MCDM)10.1109/MCDM.2013.6595441(33-40)Online publication date: Apr-2013
https://doi.org/10.1109/MCDM.2013.6595441
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Index Terms

Incorporation of decision maker's preference into evolutionary multiobjective optimization algorithms
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Heuristic function construction

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GECCO '06: Proceedings of the 8th annual conference on Genetic and evolutionary computation

July 2006

2004 pages

ISBN:1595931864

DOI:10.1145/1143997

General Chair:
Mike Cattolico
Tiger Mountain Scientific, Inc., USA

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Published: 08 July 2006

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July 8 - 12, 2006

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GECCO '06 Paper Acceptance Rate 205 of 446 submissions, 46%;

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

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Méndez MFrutos MMiguel FAguasca-Colomo R(2020)TOPSIS Decision on Approximate Pareto Fronts by Using Evolutionary Algorithms: Application to an Engineering Design ProblemMathematics10.3390/math81120728:11(2072)Online publication date: 20-Nov-2020
https://doi.org/10.3390/math8112072
Cheng RRodemann TFischer MOlhofer MJin Y(2017)Evolutionary Many-Objective Optimization of Hybrid Electric Vehicle Control: From General Optimization to Preference ArticulationIEEE Transactions on Emerging Topics in Computational Intelligence10.1109/TETCI.2017.26691041:2(97-111)Online publication date: Apr-2017
https://doi.org/10.1109/TETCI.2017.2669104
Aissanou FPetrowski A(2013)Autonomous multi-criteria decision making for route selection in a telecommunication network2013 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making (MCDM)10.1109/MCDM.2013.6595441(33-40)Online publication date: Apr-2013
https://doi.org/10.1109/MCDM.2013.6595441
Bui LAbbass HBarlow MBender A(2012)Robustness Against the Decision-Maker's Attitude to Risk in Problems With Conflicting ObjectivesIEEE Transactions on Evolutionary Computation10.1109/TEVC.2010.205144316:1(1-19)Online publication date: 1-Feb-2012
https://dl.acm.org/doi/10.1109/TEVC.2010.2051443
Mishra BDehuri SMall RGhosh A(2011)Parallel Single and Multiple Objectives Genetic AlgorithmsInternational Journal of Applied Evolutionary Computation10.4018/jaec.20110401022:2(21-57)Online publication date: 1-Apr-2011
https://dl.acm.org/doi/10.4018/jaec.2011040102
Ghandar AMichalewicz Z(2011)An experimental study of Multi-Objective Evolutionary Algorithms for balancing interpretability and accuracy in fuzzy rulebase classifiers for financial prediction2011 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr)10.1109/CIFER.2011.5953570(1-6)Online publication date: Apr-2011
https://doi.org/10.1109/CIFER.2011.5953570
Ghandar AMichalewicz Z(2011)Using cellular evolution for diversification of the balance between accurate and interpretable fuzzy knowledge bases for classification2011 IEEE Congress of Evolutionary Computation (CEC)10.1109/CEC.2011.5949790(1481-1488)Online publication date: Jun-2011
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Wagner TTrautmann H(2010)Integration of preferences in hypervolume-based multiobjective evolutionary algorithms by means of desirability functionsIEEE Transactions on Evolutionary Computation10.1109/TEVC.2010.205811914:5(688-701)Online publication date: 1-Oct-2010
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Petrowski AAissanou FBenyahia IHoucke S(2010)Multicriteria reinforcement learning based on a Russian doll method for network routing2010 5th IEEE International Conference Intelligent Systems10.1109/IS.2010.5548378(321-326)Online publication date: Jul-2010
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Sun YZhang CGao LWang X(2010)Multi-objective optimization algorithms for flow shop scheduling problem: a review and prospectsThe International Journal of Advanced Manufacturing Technology10.1007/s00170-010-3094-455:5-8(723-739)Online publication date: 21-Dec-2010
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Evolutionary multiobjective optimization and multiobjective fuzzy system design

Comparison of evolutionary multiobjective optimization with rference solution-based single-objective approach

An empirical study on the handling of overlapping solutions in evolutionary multiobjective optimization

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Evolutionary multiobjective optimization and multiobjective fuzzy system design

Comparison of evolutionary multiobjective optimization with rference solution-based single-objective approach

An empirical study on the handling of overlapping solutions in evolutionary multiobjective optimization

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