Academic Journal
An Efficient Neural Network Model with Reduction of Complexity for Solving Convex Quadratic Multi-Objective Programming Problems.
| Τίτλος: | An Efficient Neural Network Model with Reduction of Complexity for Solving Convex Quadratic Multi-Objective Programming Problems. |
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| Συγγραφείς: | Jahangiri, Mohammadreza, Nazemi, Alireza |
| Πηγή: | New Mathematics & Natural Computation; Sep2026, Vol. 22 Issue 3, p989-1024, 36p |
| Θεματικοί όροι: | Multi-objective optimization, Quadratic programming, Pareto optimum, Global optimization, Mathematical programming, Lyapunov stability, Artificial neural networks, Optimization algorithms |
| Περίληψη: | 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] |
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| Βάση Δεδομένων: | Complementary Index |
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