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
Hauptverfasser: Ansary, Md Abu Talhamainuddin, Panda, Geetanjali
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2020
Springer Nature B.V
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ISSN:1598-5865, 1865-2085
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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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ISSN:1598-5865
1865-2085
DOI:10.1007/s12190-020-01359-y