research-article

Another difficulty of inverted triangular pareto fronts for decomposition-based multi-objective algorithms

Authors:

Auraham Camacho,

Hisao IshibuchiAuthors Info & Claims

GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference

Pages 498 - 506

https://doi.org/10.1145/3377930.3390196

Published: 26 June 2020 Publication History

Abstract

A set of uniformly sampled weight vectors from a unit simplex has been frequently used in decomposition-based multi-objective algorithms. The number of the generated weight vectors is controlled by a user-defined parameter H. In the literature, good results are often reported on test problems with triangular Pareto fronts since the shape of the Pareto fronts is consistent with the distribution of the weight vectors. However, when a problem has an inverted triangular Pareto front, well-distributed solutions over the entire Pareto front are not obtained due to the inconsistency between the Pareto front shape and the weight vector distribution. In this paper, we demonstrate that the specification of H has an unexpected large effect on the performance of decomposition-based multi-objective algorithms when the test problems have inverted triangular Pareto fronts. We clearly explain why their performance is sensitive to the specification of H in an unexpected manner (e.g., H = 3 is bad but H = 4 is good for three-objective problems whereas H = 3 is good but H = 4 is bad for four-objective problems). After these discussions, we suggest a simple weight vector specification method for inverted triangular Pareto fronts.

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Cited By

Han XChao TYang MLi M(2024)A steady-state weight adaptation method for decomposition-based evolutionary multi-objective optimisationSwarm and Evolutionary Computation10.1016/j.swevo.2024.10164189(101641)Online publication date: Aug-2024
https://doi.org/10.1016/j.swevo.2024.101641
Xie LLu XLiu HHu YZhang XYang S(2024)Moboa: a proposal for multiple objective bean optimization algorithmComplex & Intelligent Systems10.1007/s40747-024-01523-y10:5(6839-6865)Online publication date: 22-Jun-2024
https://doi.org/10.1007/s40747-024-01523-y
Yang QZhan ZKwong SZhang J(2023)Multiple Populations for Multiple Objectives Framework With Bias Sorting for Many-Objective OptimizationIEEE Transactions on Evolutionary Computation10.1109/TEVC.2022.321205827:5(1340-1354)Online publication date: Oct-2023
https://doi.org/10.1109/TEVC.2022.3212058
Show More Cited By

Index Terms

Another difficulty of inverted triangular pareto fronts for decomposition-based multi-objective algorithms
1. Mathematics of computing
  1. Mathematical analysis
    1. Mathematical optimization
      1. Discrete optimization
        Optimization with randomized search heuristics
        Evolutionary algorithms

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cover image ACM Conferences

GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference

June 2020

1349 pages

ISBN:9781450371285

DOI:10.1145/3377930

General Chair:
Carlos Artemio Coello Coello
CINVESTAV-IPN

Copyright © 2020 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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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Association for Computing Machinery

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Publication History

Published: 26 June 2020

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Shenzhen Science and Technology Program
National Natural Science Foundation of China
the Program for Guangdong Introducing Innovative and Enterpreneurial Teams
the Science and Technology Innovation Committee Foundation of Shenzhen
the Program for University Key Laboratory of Guangdong Province

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GECCO '20

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SIGEVO

GECCO '20: Genetic and Evolutionary Computation Conference

July 8 - 12, 2020

Cancún, Mexico

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

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Cited By

Han XChao TYang MLi M(2024)A steady-state weight adaptation method for decomposition-based evolutionary multi-objective optimisationSwarm and Evolutionary Computation10.1016/j.swevo.2024.10164189(101641)Online publication date: Aug-2024
https://doi.org/10.1016/j.swevo.2024.101641
Xie LLu XLiu HHu YZhang XYang S(2024)Moboa: a proposal for multiple objective bean optimization algorithmComplex & Intelligent Systems10.1007/s40747-024-01523-y10:5(6839-6865)Online publication date: 22-Jun-2024
https://doi.org/10.1007/s40747-024-01523-y
Yang QZhan ZKwong SZhang J(2023)Multiple Populations for Multiple Objectives Framework With Bias Sorting for Many-Objective OptimizationIEEE Transactions on Evolutionary Computation10.1109/TEVC.2022.321205827:5(1340-1354)Online publication date: Oct-2023
https://doi.org/10.1109/TEVC.2022.3212058
He LCamacho ANan YTrivedi AIshibuchi HSrinivasan D(2023)Effects of corner weight vectors on the performance of decomposition-based multiobjective algorithmsSwarm and Evolutionary Computation10.1016/j.swevo.2023.10130579(101305)Online publication date: Jun-2023
https://doi.org/10.1016/j.swevo.2023.101305

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