Abstract

the fast packet switching (FPS) in computer networks. This paper further extends GCA to effectively solving a class of optimization problems subject to a binary constraint matrix, including the FPS problem and the traveling salesman problem (TSP) that is NP-hard. In contrast to Hopfield-type neural networks and cellular neural networks, the proposed GCA approach has the pyramid architecture and evolutionary dynamics related to multi-granularity macro-cells. This paper discusses the details regarding the dynamics and properties of the improved GCA. erms of the solution quality

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