A Discrete-Time Collaborative Neurodynamic Approach to Distributed Global Optimization
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.
Authors
- Haoen Huang (ORCID: https://orcid.org/0000-0003-0896-5192)
- Zhigang Zeng (ORCID: https://orcid.org/0000-0003-4587-3588)
- Jun Wang (ORCID: https://orcid.org/0000-0001-8708-1584)
Institutions
- City University of Hong Kong (HK)
- Huazhong University of Science and Technology (CN)
Publication Details
- Published
- 2025-02-06
- DOI
- https://doi.org/10.1109/icicip64458.2025.10898108
- Citations
- 1
- Primary Topic
- Neural Networks and Applications
- Type
- article
- Field-Weighted Citation Impact
- 2.48
Funders
- National Natural Science Foundation of China
- Fundamental Research Funds for the Central Universities