Integrating reference point, Kuhn–Tucker conditions and neural network approach for multi-objective and multi-level programming problems

R. M. Rizk-Allah () and Mahmoud A. Abo-Sinna ()
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R. M. Rizk-Allah: El-Menoufia University
Mahmoud A. Abo-Sinna: Princess Nora Bent AbdulRahman University

OPSEARCH, 2017, vol. 54, issue 4, No 1, 663-683

Abstract: Abstract In this paper, a neural network approach is constructed to solve multi-objective programming problem (MOPP) and multi-level programming problem (MLPP). The main idea is to convert the MOPP and the MLPP into an equivalent convex optimization problem. A neural network approach is then constructed for solving the obtained convex programming problem. Based on employing Lyapunov theory, the proposed neural network approach is stable in the sense of Lyapunov and it is globally convergent to an exact optimal solution of the MOPP and the MLPP. The simulation results also demonstrate that the proposed neural network is feasible and efficient.

Keywords: Karush–Kuhn–Tucker conditions; Multi-level programming; Multi-objective programming; Neural network; Asymptotic stability (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s12597-017-0299-4

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