A Collective Neurodynamic Approach to Distributed Constrained Optimization

• Published in: IEEE transaction on neural networks and learning systems Vol. 28; no. 8; pp. 1747 - 1758
• Main Authors: Liu, Qingshan, Yang, Shaofu, Wang, Jun
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.08.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Collective neurodynamics; Computer simulation; consensus; Constraints; distributed optimization; Economic models; Graph theory; Jacobian matrices; Linear programming; Matrix converters; Neural networks; Neurodynamics; Optimization; Recurrent neural networks; recurrent neural networks (RNNs); Viability
• ISSN: 2162-237X; 2162-2388
• DOI: 10.1109/TNNLS.2016.2549566

This paper presents a collective neurodynamic approach with multiple interconnected recurrent neural networks (RNNs) for distributed constrained optimization. The objective function of the distributed optimization problems to be solved is a sum of local convex objective functions, which may be nonsmooth. Subject to its local constraints, each local objective function is minimized individually by using an RNN, with consensus among others. In contrast to existing continuous-time distributed optimization methods, the proposed collective neurodynamic approach is capable of solving more general distributed optimization problems. Simulation results on three numerical examples are discussed to substantiate the effectiveness and characteristics of the proposed approach. In addition, an application to the optimal placement problem is delineated to demonstrate the viability of the approach.