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A Quantum-Behaved Neurodynamic Approach for Nonconvex Optimization with Constraints

Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122, China
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Algorithms 2019, 12(7), 138; https://doi.org/10.3390/a12070138
Received: 29 May 2019 / Revised: 27 June 2019 / Accepted: 3 July 2019 / Published: 5 July 2019
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Abstract

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. View Full-Text
Keywords: nonconvex optimization; feedback neural network; collective neurodynamic optimization; quantum-behaved particle swarm optimization nonconvex optimization; feedback neural network; collective neurodynamic optimization; quantum-behaved particle swarm optimization
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Ji, Z.; Cai, X.; Lou, X. A Quantum-Behaved Neurodynamic Approach for Nonconvex Optimization with Constraints. Algorithms 2019, 12, 138.

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