research-article

Interactive MOEA/D for multi-objective decision making

Authors:

Qingfu ZhangAuthors Info & Claims

GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation

Pages 721 - 728

https://doi.org/10.1145/2001576.2001675

Published: 12 July 2011 Publication History

Abstract

In this paper, an interactive version of the decomposition based multiobjective evolutionary algorithm (iMOEA/D) is proposed for interaction between the decision maker (DM) and the algorithm. In MOEA/D, a multi-objective problem (MOP) can be decomposed into several single-objective sub-problems. Thus, the preference incorporation mechanism in our algorithm is implemented by selecting the preferred sub-problems rather than the preferred region in the objective space. At each interaction, iMOEA/D offers a set of current solutions and asks the DM to choose the most preferred one. Then, the search will be guided to the neighborhood of the selected. iMOEA/D is tested on some benchmark problems, and various utility functions are used to simulate the DM's responses. The experimental studies show that iMOEA/D can handle the preference information very well and successfully converge to the expected preferred regions.

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Cited By

Wang YPei Y(2024)A comprehensive survey on interactive evolutionary computation in the first two decades of the 21st centuryApplied Soft Computing10.1016/j.asoc.2024.111950164(111950)Online publication date: Oct-2024
https://doi.org/10.1016/j.asoc.2024.111950
Liao FZhang SXiao DWang HZhang H(2024)An Indicator-Based Firefly Algorithm for Many-Objective OptimizationAdvanced Intelligent Computing Technology and Applications10.1007/978-981-97-5581-3_19(231-244)Online publication date: 1-Aug-2024
https://doi.org/10.1007/978-981-97-5581-3_19
Lárraga GMiettinen KSilva SPaquete L(2023)Component-based thinking in designing interactive multiobjective evolutionary methodsProceedings of the Companion Conference on Genetic and Evolutionary Computation10.1145/3583133.3596307(1693-1702)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583133.3596307
Show More Cited By

Index Terms

Interactive MOEA/D for multi-objective decision making
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Heuristic function construction

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cover image ACM Conferences

GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation

July 2011

2140 pages

ISBN:9781450305570

DOI:10.1145/2001576

Editor:
Natalio Krasnogor
University of Nottingham, UK
,
General Chair:
Pier Luca Lanzi
Politecnico di Milano, Italy

Copyright © 2011 ACM.

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Published: 12 July 2011

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GECCO '11

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GECCO '11: Genetic and Evolutionary Computation Conference

July 12 - 16, 2011

Dublin, Ireland

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

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Cited By

Wang YPei Y(2024)A comprehensive survey on interactive evolutionary computation in the first two decades of the 21st centuryApplied Soft Computing10.1016/j.asoc.2024.111950164(111950)Online publication date: Oct-2024
https://doi.org/10.1016/j.asoc.2024.111950
Liao FZhang SXiao DWang HZhang H(2024)An Indicator-Based Firefly Algorithm for Many-Objective OptimizationAdvanced Intelligent Computing Technology and Applications10.1007/978-981-97-5581-3_19(231-244)Online publication date: 1-Aug-2024
https://doi.org/10.1007/978-981-97-5581-3_19
Lárraga GMiettinen KSilva SPaquete L(2023)Component-based thinking in designing interactive multiobjective evolutionary methodsProceedings of the Companion Conference on Genetic and Evolutionary Computation10.1145/3583133.3596307(1693-1702)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583133.3596307
Li KLai GYao X(2023)Interactive Evolutionary Multiobjective Optimization via Learning to RankIEEE Transactions on Evolutionary Computation10.1109/TEVC.2023.323426927:4(749-763)Online publication date: Aug-2023
https://doi.org/10.1109/TEVC.2023.3234269
Zheng JDu ZZou JYang S(2023)A weight vector generation method based on normal distribution for preference-based multi-objective optimizationSwarm and Evolutionary Computation10.1016/j.swevo.2023.10125077(101250)Online publication date: Mar-2023
https://doi.org/10.1016/j.swevo.2023.101250
Lárraga GSaini BMiettinen K(2023)Incorporating Preference Information Interactively in NSGA-III by the Adaptation of Reference VectorsEvolutionary Multi-Criterion Optimization10.1007/978-3-031-27250-9_41(578-592)Online publication date: 9-Mar-2023
https://doi.org/10.1007/978-3-031-27250-9_41
Sato HIshibuchi H(2023)Evolutionary Many‐objective Optimization: Difficulties, Approaches, and DiscussionsIEEJ Transactions on Electrical and Electronic Engineering10.1002/tee.2379618:7(1048-1058)Online publication date: 17-Mar-2023
https://doi.org/10.1002/tee.23796
Tomczyk MKadziński M(2022)Interactive co-evolutionary multiple objective optimization algorithms for finding consensus solutions for a group of Decision MakersInformation Sciences10.1016/j.ins.2022.10.064616(157-181)Online publication date: Nov-2022
https://doi.org/10.1016/j.ins.2022.10.064
Lin XLuo WGu NZhang Q(2022)A novel dynamic reference point model for preference-based evolutionary multiobjective optimizationComplex & Intelligent Systems10.1007/s40747-022-00870-yOnline publication date: 19-Sep-2022
https://doi.org/10.1007/s40747-022-00870-y
Lin XLuo WGu NZhang Q(2022)A novel dynamic reference point model for preference-based evolutionary multiobjective optimizationComplex & Intelligent Systems10.1007/s40747-022-00860-09:2(1415-1437)Online publication date: 12-Sep-2022
https://doi.org/10.1007/s40747-022-00860-0
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