Virtual Network Embedding Based on Multi-objective Group Search Optimizer

Virtual network embedding problem is a multi-objective NP-hard problem. Various mapping objectives are often mutually restricted with each other. The existing methods are mostly converting them into a single-objective optimization, which make it difficult to balance the mapping objectives. Based on the concept of multi-objective optimization, a new virtual network embedding model is proposed in this paper. In order to balance mutual constraints of the objectives, two objective functions are defined and optimized simultaneously. Multi-objective group search optimizer is used to achieve the Pareto-optimal set, in which, the ranger searching strategy is improved to optimize the performance of the algorithm. Experimental results demonstrate that the proposed method owns good performance in terms of the revenue and acceptance ratio.