Selecting test problems from benchmark suites in constrained multiobjective optimisation

2026/06/12 by Jordan N. Cork, Tea Tušar, Bogdan Filipič · 1 voice
Computer Science · Mathematics · #Advanced Multi-Objective Optimization Algorithms #Advanced Optimization Algorithms Research #Metaheuristic Optimization Algorithms Research

paper · doi:10.1016/j.knosys.2026.116376

openalex publication_date 2026/06/12 · crossref created 2026/06/12 · openalex created_date 2026/06/13 · openalex updated_date 2026/06/16 · crossref deposited 2026/07/09 · crossref indexed 2026/07/09 · crossref issued 2026/08/01 · crossref published 2026/08/01 · crossref published-print 2026/08/01

Citations

• SciPy 1.0: fundamental algorithms for scientific computing in Python
• Pymoo: Multi-Objective Optimization in Python
• Characterization of Constrained Continuous Multiobjective Optimization Problems: A Performance Space Perspective

Discussions

• In this #OpenAccess paper we show how to select a diverse and manageable subset of test problems from numerous benchmark suites available in constrained multiobjective optimisation. doi.org/10.1016/j. [bsky, 2 points, 1 comments]