A distributed hierarchical algorithm for multi-cluster constrained optimization

Penjadwalan Pemeliharaan Trek Kereta dengan Metode Distributed Model Predictive Control

“…Dari perspektif komputasi, skema centralized memiliki kelemahan ketika jumlah variabel proses meningkat secara masif. Hal ini dikarenakan peningkatan jumlah variabel keputusan secara linear dalam masalah optimasi pemeliharaan seperti itu mengakibatkan peningkatan beban komputasi eksponensial yang diperlukan oleh DSS [7].…”

Gossip‐based distributed hierarchical algorithm for multi‐cluster constrained optimisation

“…For instance, for an interconnected multi‐area power system consisting of several heterogeneous subsystems [18, 19], the communication delay among different subsystems is much higher than the delay inside each subsystem when executing the synchronised algorithms [7–11, 13–15]. To deal with this challenge arising in multi‐cluster networks, an efficient distributed hierarchical algorithm, which is motivated by the round‐robin communication strategy [20], has been developed in [21], and the convergence accuracy of this algorithm has been studied in [22]. Compared with the preceding distributed optimisation algorithms [7–11, 13–15], the algorithm [21] can achieve a higher operating efficiency in multi‐cluster networks.…”

“…To deal with this challenge arising in multi‐cluster networks, an efficient distributed hierarchical algorithm, which is motivated by the round‐robin communication strategy [20], has been developed in [21], and the convergence accuracy of this algorithm has been studied in [22]. Compared with the preceding distributed optimisation algorithms [7–11, 13–15], the algorithm [21] can achieve a higher operating efficiency in multi‐cluster networks. Nevertheless, in this work, the inter‐cluster information is actually transmitted via a Hamilton cycle.…”

Robust cooperative control and optimization of multi-agent systems

“…For example, in [26], a projected subgradient method was developed for a constrained optimization problem. In [27], a discrete-time distributed optimization algorithm was proposed for a multi-agent system which consists of multiple clusters of agents. In [28], a randomized incremental subgradient method was developed.…”

“…, 4}) under the output feedback control law(4.36) and(4.37).Distributed optimization refers that a group of distributed agents, each having access to a local objective function, collaborate with each other to optimize a global objective function[30]. When there exist constraints that correspond to a convex set, projected gradient method[23,26,27,131] can be used to iteratively seek the optimal solution. Most of the existing literature on distributed optimization consider static objective functions.…”