Conflict avoidance(CA) plays a crucial role in guaranteeing the airspace safety. The current approaches,mostly focusing on a short-term situation which eliminates conflicts via local adjustment,cannot provide a global solution. Recently,long-term conflict avoidance approaches,which are proposed to provide solutions via strategically planning traffic flow from a global view,have attracted more attentions. With consideration of the situation in China,there are thousands of flights per day and the air route network is large and complex,which makes the long-term problem to be a large-scale combinatorial optimization problem with complex constraints. To minimize the risk of premature convergence being faced by current approaches and obtain higher quality solutions,in this work,we present an effective strategic framework based on a memetic algorithm(MA),which can markedly improve search capability via a combination of population-based global search and local improvements made by individuals. In addition,a specially designed local search operator and an adaptive local search frequency strategy are proposed to improve the solution quality. Furthermore,a fast genetic algorithm(GA) is presented as the global optimization method. Empirical studies using real traffic data of the Chinese air route network and daily flight plans show that our approach outperformed the existing approaches including the GA based approach and the cooperative coevolution based approach as well as some well-known memetic algorithm based approaches. Conflict avoidance (CA) plays a crucial role in guaranteeing the airspace safety. The current approaches, mostly focusing on a short-term situation that solved conflicts the via local adjustment, can not provide a global solution. have proposed to provide solutions via strategically planning traffic flow from a global view, have attracted more attentions. With consideration of the situation in China, there are thousands of flights for large and complex, which makes the long- To minimize the risk of premature convergence being faced by current approaches and obtain higher quality solutions, in this work, we present an effective strategic framework based on a memetic algorithm (MA ), which can markedly improve search capability via a combination of population-based global search and local improvements made by individuals. In addition, a specially designed local search operator and an adaptive local search frequency strategy are proposed to improve the solution quality. air route network and daily flight plans show that our approach outperformed the existing approaches including the GA based approach and the cooperative coevolution based approach as well as some well-known memetic algorithm based approaches.