Many-Objective Cover Problem : Discovering Few Solutions to Cover Many Objectives
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
| Original language | English |
|---|---|
| Title of host publication | Parallel Problem Solving from Nature – PPSN XVIII |
| Subtitle of host publication | 18th International Conference, PPSN 2024, Hagenberg, Austria, September 14–18, 2024, Proceedings, Part IV |
| Editors | Michael Affenzeller, Stephan M. Winkler, Anna V. Kononova, Heike Trautmann, Tea Tušar, Penousal Machado, Thomas Bäck |
| Place of Publication | Cham |
| Publisher | Springer |
| Pages | 68-82 |
| ISBN (electronic) | 978-3-031-70085-9 |
| ISBN (print) | 978-3-031-70084-2 |
| Publication status | Published - 2024 |
Publication series
| Name | Lecture Notes in Computer Science |
|---|---|
| Volume | 15151 |
| ISSN (Print) | 0302-9743 |
| ISSN (electronic) | 1611-3349 |
Conference
| Title | 18th International Conference on Parallel Problem Solving from Nature (PPSN 2024) |
|---|---|
| Location | University of Applied Sciences Upper Austria |
| Place | Austria |
| City | Hagenberg |
| Period | 14 - 18 September 2024 |
Link(s)
Abstract
Many-objective optimization (MaO) is a basic issue in various research areas. Although Pareto optimality is a common criterion for MaO, it may bring many troubles when facing a huge number (e.g., up to 100) of objectives. This paper provides a new perspective on MaO by introducing a many-objective cover problem (MaCP). Given m objectives, MaCP aims to find a solution set with size k (1 < k ≪ m) to cover all objectives (i.e., each objective can be approximately optimized by at least one solution in this set). We prove the NP-hard property of MaCP and develop a clustering-based swarm optimizer (CluSO) with a convergence guarantee to tackle MaCP. Then, we propose a decoupling many-objective test suite (DC-MaTS) with practical significance and use it to evaluate CluSO. Extensive experimental results on various test problems with up to 100 objectives demonstrate both the efficiency and effectiveness of CluSO, while also illustrating that MaCP is a feasible perspective on MaO. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Research Area(s)
- Clustering, Many-objective optimization, Multi-objective optimization, Particle swarm optimization
Citation Format(s)
Many-Objective Cover Problem: Discovering Few Solutions to Cover Many Objectives. / Liu, Yilu; Lu, Chengyu; Lin, Xi et al.
Parallel Problem Solving from Nature – PPSN XVIII: 18th International Conference, PPSN 2024, Hagenberg, Austria, September 14–18, 2024, Proceedings, Part IV. ed. / Michael Affenzeller; Stephan M. Winkler; Anna V. Kononova; Heike Trautmann; Tea Tušar; Penousal Machado; Thomas Bäck. Cham: Springer , 2024. p. 68-82 (Lecture Notes in Computer Science; Vol. 15151).
Parallel Problem Solving from Nature – PPSN XVIII: 18th International Conference, PPSN 2024, Hagenberg, Austria, September 14–18, 2024, Proceedings, Part IV. ed. / Michael Affenzeller; Stephan M. Winkler; Anna V. Kononova; Heike Trautmann; Tea Tušar; Penousal Machado; Thomas Bäck. Cham: Springer , 2024. p. 68-82 (Lecture Notes in Computer Science; Vol. 15151).
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review
