An efficient neurodynamic model to solve nonconvex nonlinear optimization problems and its applications. Issue 3 (8th December 2019)

• Record Type: Journal Article
• Authors: Moghaddas, Mohammad; Tohidi, Ghasem
• Other Names: Saen Reza Farzipoor guestEditor.; Song Malin guestEditor.; Fisher Ron guestEditor.
• Is Part Of: Expert systems. Volume 37:Issue 3(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-12-08
• Subjects: neural network -- nonconvex optimization -- p‐power convexification method -- projection function -- stability; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12498
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: This paper presents a recurrent neural network for solving nonconvex nonlinear optimization problems subject to nonlinear inequality constraints. First, the p ‐power transformation is exploited for local convexification of the Lagrangian function in nonconvex nonlinear optimization problem. Next, the proposed neural network is constructed based on the Karush–Kuhn–Tucker (KKT) optimality conditions and the projection function. An important property of this neural network is that its equilibrium point corresponds to the optimal solution of the original problem. By utilizing an appropriate Lyapunov function, it is shown that the proposed neural network is stable in the sense of Lyapunov and convergent to the global optimal solution of the original problem. Also, the sensitivity of the convergence is analysed by changing the scaling factors. Compared with other existing neural networks for such problem, the proposed neural network has more advantages such as high accuracy of the obtained solutions, fast convergence, and low complexity. Finally, simulation results are provided to show the benefits of the proposed model, which compare to or outperform existing models.