A Discrete-Time Collaborative Neurodynamic Approach to Distributed Global Optimization
Haoen Huang, Zhigang Zeng, Jun Wang · 2025
In this paper, a discrete-time projection neural network with an adaptive step size (DPNN) is proposed for distributed global optimization. The DPNN is proven to be convergent to a Karush-Kuhn-Tucker point. Several DPNNs are utilized in a collaborative neurodynamic framework for solving distributed global optimization problem. The efficacy of the collaborative neurodynamic approach with DPNNs is demonstrated through simulation results.