BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems

Jonathan Heins; Jeroen Rook; Lennart Schäpermeier; Pascal Kerschke; Jakob Bossek; Heike Trautmann

doi:10.1007/978-3-031-14714-2_14

BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems

Jonathan Heins, Jeroen Rook^*, Lennart Schäpermeier, Pascal Kerschke, Jakob Bossek, Heike Trautmann

^*Corresponding author for this work

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Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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Abstract

In multimodal multi-objective optimization (MMMOO), the focus is not solely on convergence in objective space, but rather also on explicitly ensuring diversity in decision space. We illustrate why commonly used diversity measures are not entirely appropriate for this task and propose a sophisticated basin-based evaluation (BBE) method. Also, BBE variants are developed, capturing the anytime behavior of algorithms. The set of BBE measures is tested by means of an algorithm configuration study. We show that these new measures also transfer properties of the well-established hypervolume (HV) indicator to the domain of MMMOO, thus also accounting for objective space convergence. Moreover, we advance MMMOO research by providing insights into the multimodal performance of the considered algorithms. Specifically, algorithms exploiting local structures are shown to outperform classical evolutionary multi-objective optimizers regarding the BBE variants and respective trade-off with HV.

Original language	English
Title of host publication	Parallel Problem Solving from Nature
Subtitle of host publication	PPSN XVII
Editors	Günter Rudolph, Anna V. Kononova, Hernán Aguirre, Pascal Kerschke, Gabriela Ochoa, Tea Tušar
Publisher	Springer
Pages	192-206
Number of pages	15
ISBN (Electronic)	978-3-031-14714-2
ISBN (Print)	978-3-031-14713-5
DOIs	https://doi.org/10.1007/978-3-031-14714-2_14
Publication status	Published - 2022

Publication series

Name	Lecture Notes in Computer Science
Volume	13398

Keywords

22/3 OA procedure

Access to Document

10.1007/978-3-031-14714-2_14

978-3-031-14714-2_14Final published version, 1.52 MBLicence: Taverne

Cite this

Heins, J., Rook, J., Schäpermeier, L., Kerschke, P., Bossek, J., & Trautmann, H. (2022). BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems. In G. Rudolph, A. V. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, & T. Tušar (Eds.), Parallel Problem Solving from Nature: PPSN XVII (pp. 192-206). (Lecture Notes in Computer Science; Vol. 13398). Springer. Advance online publication. https://doi.org/10.1007/978-3-031-14714-2_14

Heins, Jonathan ; Rook, Jeroen ; Schäpermeier, Lennart et al. / BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems. Parallel Problem Solving from Nature: PPSN XVII. editor / Günter Rudolph ; Anna V. Kononova ; Hernán Aguirre ; Pascal Kerschke ; Gabriela Ochoa ; Tea Tušar. Springer, 2022. pp. 192-206 (Lecture Notes in Computer Science).

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abstract = "In multimodal multi-objective optimization (MMMOO), the focus is not solely on convergence in objective space, but rather also on explicitly ensuring diversity in decision space. We illustrate why commonly used diversity measures are not entirely appropriate for this task and propose a sophisticated basin-based evaluation (BBE) method. Also, BBE variants are developed, capturing the anytime behavior of algorithms. The set of BBE measures is tested by means of an algorithm configuration study. We show that these new measures also transfer properties of the well-established hypervolume (HV) indicator to the domain of MMMOO, thus also accounting for objective space convergence. Moreover, we advance MMMOO research by providing insights into the multimodal performance of the considered algorithms. Specifically, algorithms exploiting local structures are shown to outperform classical evolutionary multi-objective optimizers regarding the BBE variants and respective trade-off with HV.",

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author = "Jonathan Heins and Jeroen Rook and Lennart Sch{\"a}permeier and Pascal Kerschke and Jakob Bossek and Heike Trautmann",

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doi = "10.1007/978-3-031-14714-2_14",

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Heins, J, Rook, J, Schäpermeier, L, Kerschke, P, Bossek, J & Trautmann, H 2022, BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems. in G Rudolph, AV Kononova, H Aguirre, P Kerschke, G Ochoa & T Tušar (eds), Parallel Problem Solving from Nature: PPSN XVII. Lecture Notes in Computer Science, vol. 13398, Springer, pp. 192-206. https://doi.org/10.1007/978-3-031-14714-2_14

BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems. / Heins, Jonathan; Rook, Jeroen; Schäpermeier, Lennart et al.
Parallel Problem Solving from Nature: PPSN XVII. ed. / Günter Rudolph; Anna V. Kononova; Hernán Aguirre; Pascal Kerschke; Gabriela Ochoa; Tea Tušar. Springer, 2022. p. 192-206 (Lecture Notes in Computer Science; Vol. 13398).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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AB - In multimodal multi-objective optimization (MMMOO), the focus is not solely on convergence in objective space, but rather also on explicitly ensuring diversity in decision space. We illustrate why commonly used diversity measures are not entirely appropriate for this task and propose a sophisticated basin-based evaluation (BBE) method. Also, BBE variants are developed, capturing the anytime behavior of algorithms. The set of BBE measures is tested by means of an algorithm configuration study. We show that these new measures also transfer properties of the well-established hypervolume (HV) indicator to the domain of MMMOO, thus also accounting for objective space convergence. Moreover, we advance MMMOO research by providing insights into the multimodal performance of the considered algorithms. Specifically, algorithms exploiting local structures are shown to outperform classical evolutionary multi-objective optimizers regarding the BBE variants and respective trade-off with HV.

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Heins J, Rook J, Schäpermeier L, Kerschke P, Bossek J, Trautmann H. BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems. In Rudolph G, Kononova AV, Aguirre H, Kerschke P, Ochoa G, Tušar T, editors, Parallel Problem Solving from Nature: PPSN XVII. Springer. 2022. p. 192-206. (Lecture Notes in Computer Science). Epub 2022 Aug 14. doi: 10.1007/978-3-031-14714-2_14