A Novel Recurrent Neural Network for Solving Nonlinear Optimization Problems With Inequality Constraints

2008 ◽  
Vol 19 (8) ◽  
pp. 1340-1353 ◽  
Author(s):  
Youshen Xia ◽  
Gang Feng ◽  
Jun Wang
Keyword(s):  
Neural Network ◽  
Nonlinear Optimization ◽  
Recurrent Neural Network ◽  
Optimization Problems ◽  
Inequality Constraints

scholarly journals A Quantum-Behaved Neurodynamic Approach for Nonconvex Optimization with Constraints

Algorithms ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 138
Author(s):  
Zheng Ji ◽  
Xu Cai ◽  
Xuyang Lou
Keyword(s):  
Neural Network ◽  
Nonconvex Optimization ◽  
Performance Feedback ◽  
High Performance ◽  
Optimization Problems ◽  
Inequality Constraints ◽  
Optimization Approach ◽  
Swarm Optimization ◽  
Transmission Shaft ◽  
Particle Swarm Method

This paper presents a quantum-behaved neurodynamic swarm optimization approach to solve the nonconvex optimization problems with inequality constraints. Firstly, the general constrained optimization problem is addressed and a high-performance feedback neural network for solving convex nonlinear programming problems is introduced. The convergence of the proposed neural network is also proved. Then, combined with the quantum-behaved particle swarm method, a quantum-behaved neurodynamic swarm optimization (QNSO) approach is presented. Finally, the performance of the proposed QNSO algorithm is evaluated through two function tests and three applications including the hollow transmission shaft, heat exchangers and crank–rocker mechanism. Numerical simulations are also provided to verify the advantages of our method.

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