摘要：The optimal embedding problem of virtual network requests, which satisfies nodes and link constraints, is a NP-hard problem. Heuristic algorithms solve the problem with the mathematical model optimization, but it fails to consider the influence of the virtual network embedding node itself on the optimal solution. So the cellular automata genetic mechanism is introduced into the problem, and the virtual network embedding algorithm based on cellular genetic algorithm (VNE-CGA) has been proposed. VNE-CGA uses the cellular automata to model the node, and replaces the "B4567/S1234" rule with the crossover operation in genetic algorithm. Through learning from neighbours to guide the individual's optimization process, VNECGA improves the inherent defects of traditional genetic algorithm. The experimental results show that the request acceptance ratio and the long-term average revenue increase about 5% and 12%.
