Momentum-Based Multiagent Approaches to Distributed Nonconvex Optimization

• 成果类型: Article
• 署名作者: Xia, Zicong; Liu, Yang; Kou, Kit Ian; Lu, Jianquan; Gui, Weihua
• 署名单位: Zhejiang Normal University; Southeast University - China; Zhejiang Normal University; Zhejiang Normal University; University of Macau; Central South University
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2024.3522188
• 发表日期: 2025
• 页码: 3331-3338
• 关键词: Optimization; CONVERGENCE; linear programming; Particle swarm optimization; Convex functions; security; scalability; Recurrent neural networks; Power system stability; Neurodynamics; Distributed nonconvex optimization; momentum-based optimization; multiagent systems (MASs)

摘要

In this article, a paradigm of momentum-based systems is introduced for nonconvex optimization. Based on the paradigm, a momentum-based system and a momentum-based multiagent system are developed for nonconvex constrained optimization and distributed nonconvex optimization, respectively, and the convergence and convergence rate to a local optimal solution are proven. In addition, a hybrid swarm intelligence algorithm is established, which consists of multiple momentum-based systems for scattering searches and a meta-heuristic rule for repositioning the states upon their local convergence. Two numerical examples are elaborated to verify and demonstrate the optimality, enhanced stability, and faster convergence of the proposed approaches.