Solving Nonlinear Equality Constrained Multiobjective Optimization Problems Using Neural Networks

This paper develops a neural network architecture and a new processing method for solving in real time, the nonlinear equality constrained multiobjective optimization problem (NECMOP), where several nonlinear objective functions must be optimized in a conflicting situation. In this processing method...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems Vol. 26; no. 10; pp. 2500 - 2520
Main Authors: Mestari, Mohammed, Benzirar, Mohammed, Saber, Nadia, Khouil, Meryem
Format: Journal Article
Language:English
Published: United States IEEE 01.10.2015
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Architecture (computers)
Computer Simulation
Constraints
Decomposition
Differential equations
Equations
Humans
Linear programming
Modularity
Multiplexing switched capacitor circuits
Neural networks
Neural Networks (Computer)
neural networks architecture
nonlinear constrained multiobjective optimization problem and scalar optimization problem (SOP)
Nonlinear Dynamics
Nonlinearity
Optimization
Pattern Recognition, Automated
Problem Solving
Real-time systems
Vectors
Multiplexing switched capacitor circuits
neural networks architecture
nonlinear constrained multiobjective optimization problem and scalar optimization problem (SOP)
ISSN:2162-237X, 2162-2388, 2162-2388
Online Access:Get full text
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Summary:This paper develops a neural network architecture and a new processing method for solving in real time, the nonlinear equality constrained multiobjective optimization problem (NECMOP), where several nonlinear objective functions must be optimized in a conflicting situation. In this processing method, the NECMOP is converted to an equivalent scalar optimization problem (SOP). The SOP is then decomposed into several-separable subproblems processable in parallel and in a reasonable time by multiplexing switched capacitor circuits. The approach which we propose makes use of a decomposition-coordination principle that allows nonlinearity to be treated at a local level and where coordination is achieved through the use of Lagrange multipliers. The modularity and the regularity of the neural networks architecture herein proposed make it suitable for very large scale integration implementation. An application to the resolution of a physical problem is given to show that the approach used here possesses some advantages of the point of algorithmic view, and provides processes of resolution often simpler than the usual techniques.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2015.2388511