Multi-objective optimization model and evolutional solution of network node matching problem

In reality, many systems can be abstracted as a network. The research results show that there are close relations between different networks. How to find out the corresponding relationship between the nodes of different networks, i.e. the node matching problem, is a topic worthy of further study. The existing network node matching methods often use a single criteria to measure the matching precision of two networks, therefore may obtain inaccurate results. In fact, the matching accuracy of two networks can be measured using different structural information, so as to improve the reliability and accuracy of the matching method. In view of this, this paper establishes a multi-objective optimization model of network node matching problem in which the matching accuracy is measured by multiple criteria. When using evolutionary algorithm to solve the model, the multiple objectives are unified into a fitness function. The experimental results show that this method can obtain better matching accuracy than single-objective method and the random method while using less running time.