Solving general convex quadratic multi-objective optimization problems via a projection neurodynamic model

A neural network model is constructed to solve convex quadratic multi-objective programming problem (CQMPP). The CQMPP is first converted into an equivalent single-objective convex quadratic programming problem by the mean of the weighted sum method, where the Pareto optimal solution (POS) are given...

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Veröffentlicht in:	Cognitive neurodynamics Jg. 18; H. 4; S. 2095 - 2110
Hauptverfasser:	Jahangiri, Mohammadreza, Nazemi, Alireza
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Dordrecht Springer Netherlands 01.08.2024 Springer Nature B.V
Schlagworte:	Algorithms Approximation Artificial Intelligence Biochemistry Biomedical and Life Sciences Biomedicine Cognitive Psychology Computer Science Dynamical systems Equilibrium Integer programming Linear programming Mathematical programming Methods Multiple objective analysis Neural networks Neurosciences Optimization Ordinary differential equations Pareto optimization Pareto optimum Quadratic programming Research Article Pareto optimal solution Stability Convex quadratic programming problem Neural networks Multi-objective optimization problem Convergence
ISSN:	1871-4080, 1871-4099
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Abstract	A neural network model is constructed to solve convex quadratic multi-objective programming problem (CQMPP). The CQMPP is first converted into an equivalent single-objective convex quadratic programming problem by the mean of the weighted sum method, where the Pareto optimal solution (POS) are given by diversifying values of weights. Then, for given various values weights, multiple projection neural networks are employded to search for Pareto optimal solutions. Based on employing Lyapunov theory, the proposed neural network approach is established to be stable in the sense of Lyapunov and it is globally convergent to an exact optimal solution of the single-objective problem. The simulation results also show that the presented model is feasible and efficient.
AbstractList	A neural network model is constructed to solve convex quadratic multi-objective programming problem (CQMPP). The CQMPP is first converted into an equivalent single-objective convex quadratic programming problem by the mean of the weighted sum method, where the Pareto optimal solution (POS) are given by diversifying values of weights. Then, for given various values weights, multiple projection neural networks are employded to search for Pareto optimal solutions. Based on employing Lyapunov theory, the proposed neural network approach is established to be stable in the sense of Lyapunov and it is globally convergent to an exact optimal solution of the single-objective problem. The simulation results also show that the presented model is feasible and efficient.A neural network model is constructed to solve convex quadratic multi-objective programming problem (CQMPP). The CQMPP is first converted into an equivalent single-objective convex quadratic programming problem by the mean of the weighted sum method, where the Pareto optimal solution (POS) are given by diversifying values of weights. Then, for given various values weights, multiple projection neural networks are employded to search for Pareto optimal solutions. Based on employing Lyapunov theory, the proposed neural network approach is established to be stable in the sense of Lyapunov and it is globally convergent to an exact optimal solution of the single-objective problem. The simulation results also show that the presented model is feasible and efficient. A neural network model is constructed to solve convex quadratic multi-objective programming problem (CQMPP). The CQMPP is first converted into an equivalent single-objective convex quadratic programming problem by the mean of the weighted sum method, where the Pareto optimal solution (POS) are given by diversifying values of weights. Then, for given various values weights, multiple projection neural networks are employded to search for Pareto optimal solutions. Based on employing Lyapunov theory, the proposed neural network approach is established to be stable in the sense of Lyapunov and it is globally convergent to an exact optimal solution of the single-objective problem. The simulation results also show that the presented model is feasible and efficient.
Author	Jahangiri, Mohammadreza Nazemi, Alireza
Author_xml	– sequence: 1 givenname: Mohammadreza surname: Jahangiri fullname: Jahangiri, Mohammadreza organization: Faculty of Mathematical Sciences, Shahrood University of Technology – sequence: 2 givenname: Alireza surname: Nazemi fullname: Nazemi, Alireza email: nazemi20042003@gmail.com organization: Faculty of Mathematical Sciences, Shahrood University of Technology
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/39104693$$D View this record in MEDLINE/PubMed
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Snippet	A neural network model is constructed to solve convex quadratic multi-objective programming problem (CQMPP). The CQMPP is first converted into an equivalent...
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SubjectTerms	Algorithms Approximation Artificial Intelligence Biochemistry Biomedical and Life Sciences Biomedicine Cognitive Psychology Computer Science Dynamical systems Equilibrium Integer programming Linear programming Mathematical programming Methods Multiple objective analysis Neural networks Neurosciences Optimization Ordinary differential equations Pareto optimization Pareto optimum Quadratic programming Research Article
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Title	Solving general convex quadratic multi-objective optimization problems via a projection neurodynamic model
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