An improved proximal method with quasi-distance for nonconvex multiobjective optimization problem

• Autorzy: Amir Fouzia , Farajzadeh Ali , Alzabut Jehad

Identyfikatory

DOI

10.1515/jaa-2021-2074

• Języki publikacji: EN

Abstrakty

EN

Multiobjective optimization is the optimization with several conflicting objective functions. However, it is generally tough to find an optimal solution that satisfies all objectives from a mathematical frame of reference. The main objective of this article is to present an improved proximal method involving quasidistance for constrained multiobjective optimization problems under the locally Lipschitz condition of the cost function. An instigation to study the proximal method with quasi distances is due to its widespread applications of the quasi distances in computer theory. To study the convergence result, Fritz John’s necessary optimality condition for weak Pareto solution is used. The suitable conditions to guarantee that the cluster points of the generated sequences are Pareto-Clarke critical points are provided.

Słowa kluczowe

EN

quasi distance; proximal method; multiobjective optimization

• Wydawca: De Gruyter
• Czasopismo: Journal of Applied Analysis
• Rocznik: 2022
• Tom: Vol. 28, nr 2
• Strony: 333--340
• Opis fizyczny: Bibliogr. 20 poz.

Twórcy

autor

Amir Fouzia

• Department of Mathematics, Naresuan University, Phitsanulok, Thailand

autor

Farajzadeh Ali

• Department of Mathematics, Razi University, Kermanshah, Iran

autor

Alzabut Jehad

• Department of Mathematics and General Sciences, Prince Sultan University, Riyadh, Saudi Arabia
• Department of Industrial Engineering, OSTIM Technical University, Ankara, Turkey

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).