A Sequential Quadratic Programming Method for Constrained Multi-objective Optimization Problems
In this article, a globally convergent sequential quadratic programming (SQP) method is developed for multi-objective optimization problems with inequality type constraints. A feasible descent direction is obtained using a linear approximation of all objective functions as well as constraint functio...
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| Veröffentlicht in: | Journal of applied mathematics & computing Jg. 64; H. 1-2; S. 379 - 397 |
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| Hauptverfasser: | , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.10.2020
Springer Nature B.V |
| Schlagworte: | |
| ISSN: | 1598-5865, 1865-2085 |
| Online-Zugang: | Volltext |
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| Zusammenfassung: | In this article, a globally convergent sequential quadratic programming (SQP) method is developed for multi-objective optimization problems with inequality type constraints. A feasible descent direction is obtained using a linear approximation of all objective functions as well as constraint functions. The sub-problem at every iteration of the sequence has feasible solution. A non-differentiable penalty function is used to deal with constraint violations. A descent sequence is generated which converges to a critical point under the Mangasarian–Fromovitz constraint qualification along with some other mild assumptions. The method is compared with a selection of existing methods on a suitable set of test problems. |
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| Bibliographie: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ISSN: | 1598-5865 1865-2085 |
| DOI: | 10.1007/s12190-020-01359-y |