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An Efficient Neural Network Model with Reduction of Complexity for Solving Convex Quadratic Multi-Objective Programming Problems.

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Title: An Efficient Neural Network Model with Reduction of Complexity for Solving Convex Quadratic Multi-Objective Programming Problems.
Authors: Jahangiri, Mohammadreza, Nazemi, Alireza
Source: New Mathematics & Natural Computation; Sep2026, Vol. 22 Issue 3, p989-1024, 36p
Subject Terms: Multi-objective optimization, Quadratic programming, Pareto optimum, Global optimization, Mathematical programming, Lyapunov stability, Artificial neural networks, Optimization algorithms
Abstract: In this paper, a neural network model is constructed to solve convex quadratic multi-objective optimization problem. First, the quadratic multi-objective problem is converted into an equivalent convex quadratic problem by the mean of the weighted sum method, where the Pareto optimal solutions are obtained by using different weights. A neural network model is then constructed for solving the obtained single objective quadratic programming problem. Based on employing Lyapunov theory, the proposed neural network approach is shown to be stable in the sense of Lyapunov and it is globally convergent for an exact optimal solution of the obtained convex optimization problem. The simulation results also demonstrate that the proposed neural network is feasible and efficient. [ABSTRACT FROM AUTHOR]
Copyright of New Mathematics & Natural Computation is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: New Mathematics & Natural Computation; Sep2026, Vol. 22 Issue 3, p989-1024, 36p
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  Data: <searchLink fieldCode="DE" term="%22Multi-objective+optimization%22">Multi-objective optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Quadratic+programming%22">Quadratic programming</searchLink><br /><searchLink fieldCode="DE" term="%22Pareto+optimum%22">Pareto optimum</searchLink><br /><searchLink fieldCode="DE" term="%22Global+optimization%22">Global optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+programming%22">Mathematical programming</searchLink><br /><searchLink fieldCode="DE" term="%22Lyapunov+stability%22">Lyapunov stability</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink>
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  Data: In this paper, a neural network model is constructed to solve convex quadratic multi-objective optimization problem. First, the quadratic multi-objective problem is converted into an equivalent convex quadratic problem by the mean of the weighted sum method, where the Pareto optimal solutions are obtained by using different weights. A neural network model is then constructed for solving the obtained single objective quadratic programming problem. Based on employing Lyapunov theory, the proposed neural network approach is shown to be stable in the sense of Lyapunov and it is globally convergent for an exact optimal solution of the obtained convex optimization problem. The simulation results also demonstrate that the proposed neural network is feasible and efficient. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of New Mathematics & Natural Computation is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1142/S179300572650050X
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 36
        StartPage: 989
    Subjects:
      – SubjectFull: Multi-objective optimization
        Type: general
      – SubjectFull: Quadratic programming
        Type: general
      – SubjectFull: Pareto optimum
        Type: general
      – SubjectFull: Global optimization
        Type: general
      – SubjectFull: Mathematical programming
        Type: general
      – SubjectFull: Lyapunov stability
        Type: general
      – SubjectFull: Artificial neural networks
        Type: general
      – SubjectFull: Optimization algorithms
        Type: general
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      – TitleFull: An Efficient Neural Network Model with Reduction of Complexity for Solving Convex Quadratic Multi-Objective Programming Problems.
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            – D: 01
              M: 09
              Text: Sep2026
              Type: published
              Y: 2026
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