skip to main content
10.1145/3520304.3533634acmconferencesArticle/Chapter ViewAbstractPublication PagesgeccoConference Proceedingsconference-collections
tutorial

Difficulties in fair performance comparison of multiobjective evolutionary algorithms

Published: 19 July 2022 Publication History
First page of PDF

References

[1]
A. Kumar et al., "A benchmark-suite of real-world constrained multi-objective optimization problems and some baseline results," Swarm and Evolutionary Computation, vol. 67, Article no. 100961, 2021.
[2]
C. He, Y. Tian, H. Wang, Y. Jin, "A repository of real-world datasets for data-driven evolutionary multiobjective optimization," Complex & Intelligent Systems, vol. 6, pp. 189--197, 2020.
[3]
H. Ishibuchi, Y. Setoguchi, H. Masuda, and Y. Nojima, "How to compare many-objective algorithms under different settings of population and archive sizes," in Proceedings of IEEE CEC 2016, pp. 1149--1156, 2016.
[4]
H. Ishibuchi, Y. Setoguchi, H. Masuda, and Y. Nojima, "Performance of decomposition-based many-objective algorithms strongly depends on Pareto front shapes," IEEE Transactions on Evolutionary Computation, vol. 21, no. 2, pp. 169--190, 2017.
[5]
H. Ishibuchi, R. Imada, Y. Setoguchi, and Y. Nojima, "How to specify a reference point in hypervolume calculation for fair performance comparison," Evolutionary Computation, vol. 26, no. 3, pp. 411--440, 2018.
[6]
H. Ishibuchi, R. Imada, Y. Setoguchi, and Y. Nojima, "Reference point specification in inverted generational distance for triangular linear Pareto front," IEEE Transactions on Evolutionary Computation, vol. 22, no. 6, pp. 961--975, 2018.
[7]
L. While, L. Bradstreet, and L. Barone, "A fast way of calculating exact hypervolumes," IEEE Transactions on Evolutionary Computation, vol. 16, no. 1, pp. 86--95, 2012.
[8]
K. Shang, H. Ishibuchi, M.-L. Zhang, and Y. Liu, "A new R2 indicator for better hypervolume approximation," in Proceedings of the Genetic and Evolutionary Computation Conference, 2018, pp. 745--752.
[9]
K. Shang, W. Chen, W. Liao, H. Ishibuchi, "HV-Net: Hypervolume approximation based on DeepSets," arXiv preprint arXiv:2203.02185 (2022).
[10]
H. Ishibuchi, L. He, and K. Shang, "Regular Pareto front shape is not realistic," in Proceedings of IEEE CEC 2019, 2019.
[11]
H. Ishibuchi, T. Matsumoto, N. Masuyama, and Y. Nojima, "Many-objective problems are not always difficult for Pareto dominance-based evolutionary algorithms," in Proceedings of 24th European Conference on Artificial Intelligence (ECAI), 2020.
[12]
H. Ishibuchi, L. M. Pang, and K. Shang, "A new framework of evolutionary multi-objective algorithms with an unbounded external archive," in Proceedings of 24th European Conference on Artificial Intelligence (ECAI), 2020.
[13]
H. Ishibuchi, L. M. Pang, K. Shang, "Difficulties in fair performance comparison of multi-objective evolutionary algorithms," IEEE Computational Intelligence Magazine, vol.17, no.1, pp. 86--101, 2022.
[14]
H. K. Singh, K. S. Bhattacharjee, and T. Ray, "Distance-based subset selection for benchmarking in evolutionary multi/many-objective optimization," IEEE Transactions on Evolutionary Computation, vol. 23, no. 5, pp. 904--912, 2019.
[15]
J. E. Fieldsend, R. M. Everson, and S. Singh, "Using unconstrained elite archive for multiobjective optimization," IEEE Transactions on Evolutionary Computation, vol. 7, no. 3, pp. 305--323, 2003.
[16]
J. Knowles, "ParEGO: A hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems", IEEE Transactions on Evolutionary Computation, vol. 10, no. 1, pp. 50--66, 2006.
[17]
K. Bringmann, T. Friedrich, and P. Klitzke, "Generic postprocessing via subset selection for hypervolume and epsilon-indicator," in Proceedings of PPSN 2014, pp. 518--527, 2014. 82
[18]
K. Deb and H. Jain, "An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, Part I: Solving problems with box constraints," IEEE Transactions on Evolutionary Computation, vol. 18, no. 4, pp. 577--601, 2014.
[19]
R. Tanabe, H. Ishibuchi, and A. Oyama, "Benchmarking multi- and many-objective evolutionary algorithms under two optimization scenarios," IEEE Access, vol. 5, pp. 19597--19619, 2017.
[20]
K. Shang et al, "Hypervolume-Optimal μ-Distributions on Line/Plane-Based Pareto Fronts in Three Dimensions," IEEE Transactions on Evolutionary Computation, vol. 26, no. 2, pp. 349--363, 2022.
[21]
R. Tanabe and H. Ishibuchi, "An analysis of quality indicators using approximated optimal distributions in a three-dimensional objective space," IEEE Transactions on Evolutionary Computation, vol. 24, no. 5, pp. 853--867, 2020.
[22]
R. Tanabe and H. Ishibuchi, "An easy-to-use real-world multi-objective optimization problem suite," Applied Soft Computing, vol. 89, Article no. 106078, 2020.
[23]
S. Mostaghim and H. Schmeck, "Distance based ranking in many-objective particle swarm optimization," in Proceedings of PPSN 2008, pp. 753--762, 2008.
[24]
T. Wagner, N. Beume, and B. Naujoks, "Pareto-, aggregation-, and indicator-based methods in many-objective optimization," in Proceedings of EMO 2007, pp. 742--756, 2007.
[25]
W. Chen, H. Ishibuchi, and K. Shang, "Fast greedy subset selection from large candidate solution sets in evolutionary multi-objective optimization," IEEE Transactions on Evolutionary Computation (Early Access).
[26]
X. Zhang, Y. Tian, R. Cheng, and Y. Jin, "A decision variable clustering-based evolutionary algorithm for large-scale many-objective optimization," IEEE Transactions on Evolutionary Computation, vol. 22, no. 1, pp. 97--112, 2018.

Cited By

View all
  • (2024)Diagnostic benchmarking of many-objective evolutionary algorithms for real-world problemsEngineering Optimization10.1080/0305215X.2024.238181857:1(287-308)Online publication date: 22-Aug-2024
  • (2024)Constrained multi-objective optimization evolutionary algorithm for real-world continuous mechanical design problemsEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.108673135(108673)Online publication date: Sep-2024

Index Terms

  1. Difficulties in fair performance comparison of multiobjective evolutionary algorithms
        Index terms have been assigned to the content through auto-classification.

        Recommendations

        Comments

        Information & Contributors

        Information

        Published In

        cover image ACM Conferences
        GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference Companion
        July 2022
        2395 pages
        ISBN:9781450392686
        DOI:10.1145/3520304
        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: 19 July 2022

        Permissions

        Request permissions for this article.

        Check for updates

        Qualifiers

        • Tutorial

        Funding Sources

        • the Program for Guangdong Introducing Innovative and Enterpreneurial Teams
        • National Natural Science Foundation of China
        • Shenzhen Science and Technology Program
        • Guangdong Provincial Key Laboratory
        • The Stable Support Plan Program of Shenzhen Natural Science Fund

        Conference

        GECCO '22
        Sponsor:

        Acceptance Rates

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

        Contributors

        Other Metrics

        Bibliometrics & Citations

        Bibliometrics

        Article Metrics

        • Downloads (Last 12 months)56
        • Downloads (Last 6 weeks)2
        Reflects downloads up to 28 Feb 2025

        Other Metrics

        Citations

        Cited By

        View all
        • (2024)Diagnostic benchmarking of many-objective evolutionary algorithms for real-world problemsEngineering Optimization10.1080/0305215X.2024.238181857:1(287-308)Online publication date: 22-Aug-2024
        • (2024)Constrained multi-objective optimization evolutionary algorithm for real-world continuous mechanical design problemsEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.108673135(108673)Online publication date: Sep-2024

        View Options

        Login options

        View options

        PDF

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader

        Figures

        Tables

        Media

        Share

        Share

        Share this Publication link

        Share on social media