Momentum-Based Multiagent Approaches to Distributed Nonconvex Optimization

• Published in: IEEE transactions on automatic control Vol. 70; no. 5; pp. 3331 - 3338
• Main Authors: Xia, Zicong, Liu, Yang, Kou, Kit Ian, Lu, Jianquan, Gui, Weihua
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.05.2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Convergence; Convex functions; Distributed nonconvex optimization; Heuristic methods; Linear programming; Momentum; momentum-based optimization; Multiagent systems; multiagent systems (MASs); Neurodynamics; Optimization; Particle swarm optimization; Power system stability; Recurrent neural networks; Scalability; Security; Swarm intelligence
• ISSN: 0018-9286; 1558-2523
• DOI: 10.1109/TAC.2024.3522188

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.