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|Referiert=True | |Referiert=True | ||
| + | |BibTex-ID=shukla2013theory | ||
|Title=Theory and Algorithms for Finding Knees | |Title=Theory and Algorithms for Finding Knees | ||
|Year=2013 | |Year=2013 | ||
| − | |Booktitle= | + | |Booktitle=Evolutionary Multi-Criterion Optimization |
| − | |Publisher=Springer | + | |Pages=156-170 |
| − | |Series= | + | |Publisher=Springer Berlin Heidelberg |
| − | | | + | |Editor=Purshouse, Robin C. and Fleming, Peter J. and Fonseca, Carlos M. and Greco, Salvatore and Shaw, Jane |
| + | |Series=Lecture Notes in Computer Science | ||
| + | |Volume=7811 | ||
}} | }} | ||
{{Publikation Details | {{Publikation Details | ||
|Abstract=A multi-objective optimization problem involves multiple and conflicting objectives. These conflicting objectives give rise to a set of Pareto optimal solutions. However, not all the members of the Pareto optimal set have equally nice properties. The classical concept of proper Pareto optimality is a way of characterizing good Pareto optimal solutions. In this paper, we metrize this concept to induce an ordering on the Pareto optimal set. The use of this metric allows us to define a proper knee region, which contains solutions below a user-specified threshold metric. We theoretically analyze past definitions of knee points, and in particular, reformulate a commonly used nonlinear program, to achieve convergence results. Additionally, mathematical properties of the proper knee region are investigated. We also develop two multi-objective evolutionary algorithms towards finding proper knees and present simulation results on a number of test problems. | |Abstract=A multi-objective optimization problem involves multiple and conflicting objectives. These conflicting objectives give rise to a set of Pareto optimal solutions. However, not all the members of the Pareto optimal set have equally nice properties. The classical concept of proper Pareto optimality is a way of characterizing good Pareto optimal solutions. In this paper, we metrize this concept to induce an ordering on the Pareto optimal set. The use of this metric allows us to define a proper knee region, which contains solutions below a user-specified threshold metric. We theoretically analyze past definitions of knee points, and in particular, reformulate a commonly used nonlinear program, to achieve convergence results. Additionally, mathematical properties of the proper knee region are investigated. We also develop two multi-objective evolutionary algorithms towards finding proper knees and present simulation results on a number of test problems. | ||
| + | |ISBN=978-3-642-37139-4 | ||
| + | |Link=http://dx.doi.org/10.1007/978-3-642-37140-0_15 | ||
| + | |DOI Name=10.1007/978-3-642-37140-0_15 | ||
|Forschungsgruppe=Effiziente Algorithmen | |Forschungsgruppe=Effiziente Algorithmen | ||
}} | }} | ||
Aktuelle Version vom 15. April 2015, 13:57 Uhr
Theory and Algorithms for Finding Knees
Theory and Algorithms for Finding Knees
Published: 2013
Herausgeber: Purshouse, Robin C. and Fleming, Peter J. and Fonseca, Carlos M. and Greco, Salvatore and Shaw, Jane
Buchtitel: Evolutionary Multi-Criterion Optimization
Ausgabe: 7811
Reihe: Lecture Notes in Computer Science
Seiten: 156-170
Verlag: Springer Berlin Heidelberg
Referierte Veröffentlichung
BibTeX
Kurzfassung
A multi-objective optimization problem involves multiple and conflicting objectives. These conflicting objectives give rise to a set of Pareto optimal solutions. However, not all the members of the Pareto optimal set have equally nice properties. The classical concept of proper Pareto optimality is a way of characterizing good Pareto optimal solutions. In this paper, we metrize this concept to induce an ordering on the Pareto optimal set. The use of this metric allows us to define a proper knee region, which contains solutions below a user-specified threshold metric. We theoretically analyze past definitions of knee points, and in particular, reformulate a commonly used nonlinear program, to achieve convergence results. Additionally, mathematical properties of the proper knee region are investigated. We also develop two multi-objective evolutionary algorithms towards finding proper knees and present simulation results on a number of test problems.
ISBN: 978-3-642-37139-4
Weitere Informationen unter: Link
DOI Link: 10.1007/978-3-642-37140-0_15
Evolutionäre Algorithmen, Multikriterielle Optimierung, Globale Optimierung