To enhance the efficiency of optimization-based algorithms for detecting community structures in networks, a novel algorithm was designed by utilizing the small-world effects to accelerate biogeography-based optimization process for community detection. First, based on matrix random coding, the problems of detecting community structures in networks were embedded into the framework of biogeography-based optimization. Community structures were searched evolutionarily and globally corresponding to the maximal modularity in habitat. Then, a migration evolutionary strategy was introduced based on the small-world effects, which can accelerate the information exchange process of the proposed evolutionary algorithm. Finally, tests on real-world and computer-generated networks were conducted using the proposed algorithm. Results show that the small-world effects reduced the convergence time of the algorithm for community detection, and the values of the modularity and the normalized mutual information were both high when applying the proposed algorithm to the real-world and the computer-generated networks. The topology structures of information exchanges could optimize the efficiency of the evolutionary algorithm. Therefore, the algorithm adopting small-world effects to accelerate biogeography-based optimization for network community detection was proved feasible and effective.