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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