Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/315321 
Erscheinungsjahr: 
2024
Quellenangabe: 
[Journal:] Journal of Global Optimization [ISSN:] 1573-2916 [Volume:] 89 [Issue:] 3 [Publisher:] Springer US [Place:] New York, NY [Year:] 2024 [Pages:] 745-776
Verlag: 
Springer US, New York, NY
Zusammenfassung: 
We propose a multi-swarm approach to approximate the Pareto front of general multi-objective optimization problems that is based on the consensus-based optimization method (CBO). The algorithm is motivated step by step beginning with a simple extension of CBO based on fixed scalarization weights. To overcome the issue of choosing the weights we propose an adaptive weight strategy in the second modeling step. The modeling process is concluded with the incorporation of a penalty strategy that avoids clusters along the Pareto front and a diffusion term that prevents collapsing swarms. Altogether the proposed K -swarm CBO algorithm is tailored for a diverse approximation of the Pareto front and, simultaneously, the efficient set of general non-convex multi-objective problems. The feasibility of the approach is justified by analytic results, including convergence proofs, and a performance comparison to the well-known non-dominated sorting genetic algorithms NSGA2 and NSGA3 as well as the recently proposed one-swarm approach for multi-objective problems involving consensus-based optimization.
Schlagwörter: 
Multiobjective optimization
Consensus-based methods
Global optimization
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