BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems
Research output: Contribution to book/Conference proceedings/Anthology/Report › Conference contribution › Contributed › peer-review
Contributors
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 language | English |
---|---|
Title of host publication | 17th International Conference on Parallel Problem Solving from Nature |
Number of pages | 15 |
Publication status | Published - 2022 |
Peer-reviewed | Yes |
External IDs
Scopus | 85136946958 |
---|---|
ORCID | /0000-0002-3571-667X/work/142236653 |
ORCID | /0000-0003-3929-7465/work/142241489 |
Keywords
ASJC Scopus subject areas
Keywords
- Anytime behavior, Benchmarking, Continuous optimization, Multi-objective optimization, Multimodality, Performance metric