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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Bibliographic Details
Published in:Cognitive neurodynamics Vol. 18; no. 4; pp. 2095 - 2110
Main Authors: Jahangiri, Mohammadreza, Nazemi, Alireza
Format: Journal Article
Language:English
Published: Dordrecht Springer Netherlands 01.08.2024
Springer Nature B.V
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:1871-4080
1871-4099
DOI:10.1007/s11571-023-09998-0