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

Research output: Contribution to book/Conference proceedings/Anthology/ReportConference contributionContributedpeer-review

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.

Details

Original languageEnglish
Title of host publication17th International Conference on Parallel Problem Solving from Nature
Number of pages15
Publication statusPublished - 2022
Peer-reviewedYes

External IDs

Scopus 85136946958
ORCID /0000-0002-3571-667X/work/142236653
ORCID /0000-0003-3929-7465/work/142241489

Keywords

Keywords

  • Anytime behavior, Benchmarking, Continuous optimization, Multi-objective optimization, Multimodality, Performance metric