A Quantum-Behaved Neurodynamic Approach for Nonconvex Optimization with Constraints

Algorithms

Zheng Ji¹  Xu Cai¹  Xuyang Lou¹ 

[1] Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122, China;

关键词: nonconvex optimization;    feedback neural network;    collective neurodynamic optimization;    quantum-behaved particle swarm optimization;   

DOI  :  10.3390/a12070138

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown