An event-triggered collaborative neurodynamic approach to distributed global optimization. (English) Zbl 1532.90088
Summary: 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.
MSC:
| 90C26 | Nonconvex programming, global optimization |
| 68T05 | Learning and adaptive systems in artificial intelligence |
Keywords:
distributed optimization; global optimization; event-triggered communication; recurrent neural networks; collaborative neurodynamic optimization; HVAC systemsReferences:
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