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This paper presents an adaptive clustering algorithm based on power system network topology, initial power flow and given architecture. The sizes of the small cliques are derived using multi-constraint and multi-objective graph partitioning theory. The vertices of the graph represent units of computation, and the edges encode data dependencies. Tests for a 39-bus network, a 1056-bus network, a 3872-bus network and a 10188-bus network are reported, and the results show that the cluster-based partitioning produces smaller hyper-edge cut size and higher speedup than the traditional direct partitioning. An application to 3872-bus network shows that by the improved tree-based partitioning the speedup is improved by up to 51% compared to the traditional tree-based approach. Moreover, the results show that the partitioning results of improved tree-based partitioning algorithm and improved graph-based one are comparable. These results suggest that adaptive architecture- aware clustering algorithm can be combined with heterogeneous and changing computing resources.