Distributed Aggregative Optimization of High-Order Heterogeneous Integrator Systems

This article investigates the distributed aggregative optimization (DAO) problem for high-order heterogeneous integrator systems over undirected communication networks. Each agent’s local objective function incorporates both private decision variables and global aggregative information. Then, a novel DAO algorithm is designed. It is rigorously established that each agent converges to the global optimum at an exponentially fast rate. Finally, the efficiency of the algorithm is demonstrated by numerical examples.