A novel approach for solving constrained nonlinear optimization problems using neurofuzzy systems

2002 ◽  
Author(s):  
I. Nunes da Silva ◽  
A. Nunes de Souza ◽  
M.E. Bordon
Keyword(s):  
Nonlinear Optimization ◽  
Optimization Problems ◽  
Constrained Nonlinear Optimization ◽  
Novel Approach ◽  
Neurofuzzy Systems

A NOVEL APPROACH FOR SOLVING CONSTRAINED NONLINEAR OPTIMIZATION PROBLEMS USING NEUROFUZZY SYSTEMS

2001 ◽  
Vol 11 (03) ◽  
pp. 281-286
Author(s):  
IVAN NUNES DA SILVA ◽  
ANDRÉ NUNES DE SOUZA ◽  
MÁRIO EDUARDO BORDON
Keyword(s):  
Nonlinear Optimization ◽  
Fuzzy Logic Controller ◽  
Optimization Problems ◽  
Equilibrium Points ◽  
Hopfield Network ◽  
Convergence Time ◽  
Constrained Nonlinear Optimization ◽  
Novel Approach ◽  
Bounded Variables ◽  
Time Simulation

A neural network model for solving constrained nonlinear optimization problems with bounded variables is presented in this paper. More specifically, a modified Hopfield network is developed and its internal parameters are computed using the valid-subspace technique. These parameters guarantee the convergence of the network to the equilibrium points. The network is shown to be completely stable and globally convergent to the solutions of constrained nonlinear optimization problems. A fuzzy logic controller is incorporated in the network to minimize convergence time. Simulation results are presented to validate the proposed approach.

A new hybrid algorithm to solve bound-constrained nonlinear optimization problems

2020 ◽  
Vol 32 (16) ◽  
pp. 12427-12452 ◽  
Author(s):  
Avijit Duary ◽  
Md Sadikur Rahman ◽  
Ali Akbar Shaikh ◽  
Seyed Taghi Akhavan Niaki ◽  
Asoke Kumar Bhunia
Keyword(s):  
Nonlinear Optimization ◽  
Hybrid Algorithm ◽  
Optimization Problems ◽  
Constrained Nonlinear Optimization

Consistent Updating of Constrained Parametric Curves in a Variational Design System

1997 ◽  
Author(s):  
A. Vincent Huffaker ◽  
Leonid Charny
Keyword(s):  
Nonlinear Optimization ◽  
Degrees Of Freedom ◽  
Necessary Conditions ◽  
Optimization Problems ◽  
Design System ◽  
Systems Of Nonlinear Equations ◽  
Parametric Curves ◽  
Variational Design ◽  
Constrained Nonlinear Optimization ◽  
Design Consistency

Abstract This paper demonstrates techniques to allow relations on parametric curves in a variational design system. Constraints on the curves, which are normally represented as constrained nonlinear optimization problems, are reduced to systems of nonlinear equations (using the necessary conditions of the NLP). Additional degrees of freedom are constrained through fairing the curve and the resulting NLP is also reduced to its necessary conditions. Although the solution set of the necessary conditions contains the optimum, it contains many other solutions as well. The COAST design consistency algorithm is reviewed and then extended to handle consistency when constraints take the form of relations between objects. An example is given involving distance constraints on Bezier curves.

scholarly journals Opposition-Based Barebones Particle Swarm for Constrained Nonlinear Optimization Problems

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Hui Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Nonlinear Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Constrained Nonlinear Optimization ◽  
Opposition Based Learning ◽  
Adaptive Penalty ◽  
Adaptive Penalty Method

This paper presents a modified barebones particle swarm optimization (OBPSO) to solve constrained nonlinear optimization problems. The proposed approach OBPSO combines barebones particle swarm optimization (BPSO) and opposition-based learning (OBL) to improve the quality of solutions. A novel boundary search strategy is used to approach the boundary between the feasible and infeasible search region. Moreover, an adaptive penalty method is employed to handle constraints. To verify the performance of OBPSO, a set of well-known constrained benchmark functions is used in the experiments. Simulation results show that our approach achieves a promising performance.

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