Optimization of Network Disintegration Strategy based on Tabu-Genetic Hybrid Search Algorithm

Abstract

Network disintegration has been an important research hotspot in complex networks. Attacking or removing the nodes or edges in the network will cause the network function to decline in varying degrees and our research goal is to find the attack strategy that causes the most damage to the network, which is called the optimal attack strategy. According to different objects, network disintegration methods can be divided into node-based attack strategy and edge-based attack strategy. This paper presents an optimization model for the optimal edge attack strategy and the optimal edge attack strategy is obtained by using the tabu-genetic hybrid heuristic search algorithm to solve the optimization model. The quality of the solution obtained by the hybrid algorithm is verified by comparing the disintegration effect of hybrid algorithm, genetic algorithm and tabu search algorithm on real-world networks. Numerical experiments suggest that the global search ability of tabu-genetic hybrid algorithm is better than that of single algorithm, and its solution is closer to the optimal edge attack strategy.