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An infeasible interior-point technique to generate the nondominated set for multiobjective optimization problems

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An infeasible interior-point technique to generate the nondominated set for multiobjective optimization problems. / Jauny; Ghosh, Debdas; Ansari, Qamrul Hasan et al.
In: Computers and Operations Research, Vol. 155, 106236, 31.07.2023.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Jauny, Ghosh, D, Ansari, QH, Ehrgott, M & Upadhayay, A 2023, 'An infeasible interior-point technique to generate the nondominated set for multiobjective optimization problems', Computers and Operations Research, vol. 155, 106236. https://doi.org/10.1016/j.cor.2023.106236

APA

Jauny, Ghosh, D., Ansari, Q. H., Ehrgott, M., & Upadhayay, A. (2023). An infeasible interior-point technique to generate the nondominated set for multiobjective optimization problems. Computers and Operations Research, 155, Article 106236. https://doi.org/10.1016/j.cor.2023.106236

Vancouver

Jauny, Ghosh D, Ansari QH, Ehrgott M, Upadhayay A. An infeasible interior-point technique to generate the nondominated set for multiobjective optimization problems. Computers and Operations Research. 2023 Jul 31;155:106236. Epub 2023 Mar 31. doi: 10.1016/j.cor.2023.106236

Author

Jauny ; Ghosh, Debdas ; Ansari, Qamrul Hasan et al. / An infeasible interior-point technique to generate the nondominated set for multiobjective optimization problems. In: Computers and Operations Research. 2023 ; Vol. 155.

Bibtex

@article{0b95a0913263420d83f23cebd6c04886,
title = "An infeasible interior-point technique to generate the nondominated set for multiobjective optimization problems",
abstract = "In this paper, an infeasible interior-point technique is proposed to generate the nondominated set of nonlinear multi-objective optimization problems with the help of the direction-based cone method. We derive the proposed method for both convex and nonconvex problems. In order to solve the parametric optimization problems of the cone method, the infeasible interior-point method starts with an initial iterate outside the feasible region, and then gradually reduces the primal and dual infeasibility measures and the objective function value across the iterations with the help of a merit function. Estimates of the reduction of primal and dual infeasibility parameters per iteration are given. The convergence analysis of the method and an estimate of the number of iterations to reach an  ϵ -precise solution are also provided. We provide the performance of the proposed methods on a variety of convex and nonconvex multi-objective test problems. Performance comparison between the proposed method and popular existing solvers is provided with respect to two performance measures and the corresponding relative efficiency measures. The reduction of a combined infeasibility measure, as the iterations progress, on the test problems is also shown graphically.",
author = "Jauny and Debdas Ghosh and Ansari, {Qamrul Hasan} and Matthias Ehrgott and Ashutosh Upadhayay",
year = "2023",
month = jul,
day = "31",
doi = "10.1016/j.cor.2023.106236",
language = "English",
volume = "155",
journal = "Computers and Operations Research",
issn = "0305-0548",
publisher = "Elsevier Ltd",

}

RIS

TY - JOUR

T1 - An infeasible interior-point technique to generate the nondominated set for multiobjective optimization problems

AU - Jauny, null

AU - Ghosh, Debdas

AU - Ansari, Qamrul Hasan

AU - Ehrgott, Matthias

AU - Upadhayay, Ashutosh

PY - 2023/7/31

Y1 - 2023/7/31

N2 - In this paper, an infeasible interior-point technique is proposed to generate the nondominated set of nonlinear multi-objective optimization problems with the help of the direction-based cone method. We derive the proposed method for both convex and nonconvex problems. In order to solve the parametric optimization problems of the cone method, the infeasible interior-point method starts with an initial iterate outside the feasible region, and then gradually reduces the primal and dual infeasibility measures and the objective function value across the iterations with the help of a merit function. Estimates of the reduction of primal and dual infeasibility parameters per iteration are given. The convergence analysis of the method and an estimate of the number of iterations to reach an  ϵ -precise solution are also provided. We provide the performance of the proposed methods on a variety of convex and nonconvex multi-objective test problems. Performance comparison between the proposed method and popular existing solvers is provided with respect to two performance measures and the corresponding relative efficiency measures. The reduction of a combined infeasibility measure, as the iterations progress, on the test problems is also shown graphically.

AB - In this paper, an infeasible interior-point technique is proposed to generate the nondominated set of nonlinear multi-objective optimization problems with the help of the direction-based cone method. We derive the proposed method for both convex and nonconvex problems. In order to solve the parametric optimization problems of the cone method, the infeasible interior-point method starts with an initial iterate outside the feasible region, and then gradually reduces the primal and dual infeasibility measures and the objective function value across the iterations with the help of a merit function. Estimates of the reduction of primal and dual infeasibility parameters per iteration are given. The convergence analysis of the method and an estimate of the number of iterations to reach an  ϵ -precise solution are also provided. We provide the performance of the proposed methods on a variety of convex and nonconvex multi-objective test problems. Performance comparison between the proposed method and popular existing solvers is provided with respect to two performance measures and the corresponding relative efficiency measures. The reduction of a combined infeasibility measure, as the iterations progress, on the test problems is also shown graphically.

U2 - 10.1016/j.cor.2023.106236

DO - 10.1016/j.cor.2023.106236

M3 - Journal article

VL - 155

JO - Computers and Operations Research

JF - Computers and Operations Research

SN - 0305-0548

M1 - 106236

ER -