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

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...

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Vydáno v:Opsearch Ročník 54; číslo 4; s. 663 - 683
Hlavní autoři: Rizk-Allah, R. M., Abo-Sinna, Mahmoud A.
Médium: Journal Article
Jazyk:angličtina
Vydáno: New Delhi Springer India 01.12.2017
Springer Nature B.V
Témata:
Application Article
Business and Management
Computer simulation
Convexity
Kuhn-Tucker method
Management
Mathematical programming
Mathematics
Multiple objective analysis
Neural networks
Operations research
Operations Research/Decision Theory
Optimization
Simulation
Multi-level programming
Asymptotic stability
Multi-objective programming
Neural network
Karush–Kuhn–Tucker conditions
ISSN:0030-3887, 0975-0320
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Shrnutí: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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:0030-3887
0975-0320
DOI:10.1007/s12597-017-0299-4