Study of multiobjective fractional variational formulation using <i>η</i>-Approximation Method

This paper investigates the idea of the η-approximated technique for converting the nonlinear and nonconvex multiobjective fractional variational dual problems (MFP) and (MFD) with inequality constraints to the linear and convex counterparts of the problems, namely, (MFP)η and (MFD)η, respectively. Weak, strong, and converse duality theorems are obtained for the original as well as the η-approximated dual pair under invexity for weak Pareto as well as Pareto solutions. Furthermore, the connection between the original and modified problems has also been established. A suitable numerical example is constructed to bolster the research paper.

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Publication Details

Journal
Contemporary Mathematics
Published
2024-11-20
DOI
https://doi.org/10.37256/cm.5420245031
Primary Topic
Optimization and Mathematical Programming
Type
article
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article

Study of multiobjective fractional variational formulation using η-Approximation Method

Sony Khatri, Purusotham Singamsetty
Contemporary Mathematics
Optimization and Mathematical Programming
article

Study of multiobjective fractional variational formulation using η-Approximation Method

Sony Khatri, Purusotham Singamsetty
article en

Abstract

This paper investigates the idea of the η-approximated technique for converting the nonlinear and nonconvex multiobjective fractional variational dual problems (MFP) and (MFD) with inequality constraints to the linear and convex counterparts of the problems, namely, (MFP)η and (MFD)η, respectively. Weak, strong, and converse duality theorems are obtained for the original as well as the η-approximated dual pair under invexity for weak Pareto as well as Pareto solutions. Furthermore, the connection between the original and modified problems has also been established. A suitable numerical example is constructed to bolster the research paper.

Contemporary Mathematics
Vellore Institute of Technology University (IN)
Openalex Percentile: Top 76%
Optimization and Mathematical Programming
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