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

DOI10.1007/S12597-017-0299-4zbMath1391.90568OpenAlexW2581890798MaRDI QIDQ724383

Mahmoud A. Abo-Sinna, Rizk M. Rizk-Allah

Publication date: 25 July 2018

Published in: Opsearch (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s12597-017-0299-4

zbMATH Keywords

neural networkasymptotic stabilitymulti-objective programmingKarush-Kuhn-Tucker conditionsmulti-level programming

Mathematics Subject Classification ID

Convex programming (90C25) Multi-objective and goal programming (90C29) Nonlinear programming (90C30) Approximation methods and heuristics in mathematical programming (90C59)

Related Items (4)

A comparative study of two optimization approaches for solving bi-level multi-objective linear fractional programming problem ⋮ Integrating reference point, Kuhn-Tucker conditions and neural network approach for multi-objective and multi-level programming problems ⋮ Decomposition of parametric space for bi-objective optimization problem using neural network approach ⋮ Bilevel Optimization: Theory, Algorithms, Applications and a Bibliography

Uses Software

• LINDO
• LINGO

Cites Work

• Integrating reference point, Kuhn-Tucker conditions and neural network approach for multi-objective and multi-level programming problems
• A feedback neural network for solving convex constraint optimization problems
• Hierarchical optimization: An introduction
• Multiple objective decision making - methods and applications. A state- of-the-art survey. In collaboration with Sudhakar R. Paidy and Kwangsun Yoon
• Practical bilevel optimization. Algorithms and applications
• TOPSIS for MODM
• Interactive multiple objective programming using Tchebycheff programs and artificial neural networks
• Extensions of TOPSIS for multi-objective large-scale nonlinear programming problems
• A multi-level nonlinear multi-objective decision-making under fuzziness
• Multi-level decision making. Models, methods and applications
• Bilevel linear programming
• Interactive balance space approach for solving multi-level multi-objective programming problems
• Pareto optimality for bi-level programming problem with fuzzy parameters.
• A bi-level non-linear multi-objective decision making under fuzziness.
• A hybrid neural network approach to bilevel programming problems
• A new neural network for solving nonlinear projection equations
• A Mathematical Programming Model of Decentralized Multi-Level Systems
• A Representation and Economic Interpretation of a Two-Level Programming Problem
• A projection neural network and its application to constrained optimization problems
• A high performance neural network for solving nonlinear programming problems with hybrid constraints
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