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In order to improve the accuracy of the community detection in complex networks, this paper proposed a multiobjective community detection based on adaptive memetic algorithm. In global search, the algorithm applied the Logistic function to set the corresponding crossover probability and mutation probability. and turned the multi-objective optimization problem into minimal optimization of two objectives called kernel K-means( KKM) and ratio cut( RC) at the same time. In local search. it constituted the local optimization target of weights of two objective functions and used a hill-climbing strategy to find the best individual. Experiments on synthetic and real life networks show that, compared with five algorithms based on GAs( genetic algorithms) and Fast Modularity algorithm,the proposed algorithm can effectively improve the accuracy of the community detection and has certain advantages in solving community detection problems in complex networks. [ABSTRACT FROM AUTHOR]