School of Mathematical Sciences, Zhejiang Normal University, Jinhua 321004, China; School of Mathematics, Southeast University, Nanjing 210096, China.
School of Mathematical Sciences, Zhejiang Normal University, Jinhua 321004, China; Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua 321004, China.
Neural Netw. 2024 Jan;169:181-190. doi: 10.1016/j.neunet.2023.10.022. Epub 2023 Oct 19.
In this paper, we propose an event-triggered collaborative neurodynamic approach to distributed global optimization in the presence of nonconvexity. We design a projection neural network group consisting of multiple projection neural networks coupled via a communication network. We prove the convergence of the projection neural network group to Karush-Kuhn-Tucker points of a given global optimization problem. To reduce communication bandwidth consumption, we adopt an event-triggered mechanism to liaise with other neural networks in the group with the Zeno behavior being precluded. We employ multiple projection neural network groups for scattered searches and re-initialize their states using a meta-heuristic rule in the collaborative neurodynamic optimization framework. In addition, we apply the collaborative neurodynamic approach for distributed optimal chiller loading in a heating, ventilation, and air conditioning system.
本文提出了一种事件触发的协同神经动力学方法，用于解决非凸环境下的分布式全局优化问题。我们设计了一个由多个投影神经网络组成的投影神经网络组，通过一个通信网络进行耦合。我们证明了投影神经网络组收敛于给定全局优化问题的 Karush-Kuhn-Tucker 点。为了降低通信带宽消耗，我们采用事件触发机制与组内的其他神经网络进行通信，同时避免了 Zeno 行为。我们在协同神经动力学优化框架中使用多个投影神经网络组进行分散搜索，并使用启发式规则重新初始化它们的状态。此外，我们还将协同神经动力学方法应用于暖通空调系统中分布式最优冷水机组负荷的优化。
