@inproceedings{1eadba919cfb46e2888744f5666b16f8,
title = "BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems",
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.",
keywords = "22/3 OA procedure",
author = "Jonathan Heins and Jeroen Rook and Lennart Sch{\"a}permeier and Pascal Kerschke and Jakob Bossek and Heike Trautmann",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.",
year = "2022",
doi = "10.1007/978-3-031-14714-2_14",
language = "English",
isbn = "978-3-031-14713-5",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "192--206",
editor = "G{\"u}nter Rudolph and Kononova, {Anna V.} and Hern{\'a}n Aguirre and Pascal Kerschke and Gabriela Ochoa and Tea Tu{\v s}ar",
booktitle = "Parallel Problem Solving from Nature",
}