Learning brain effective connectivity (EC) networks is an important topic within the community of human brain connectome. It is of great significance for the early diagnosis and pathological study of brain diseases to accurately identify the brain EC network structure. This paper combines the Firefly Algorithm (FA) with Bayesian network

and proposes a new method to learn brain EC networks by FA with a reproductive mechanism. The new method uses K2 score as the evaluation method of absolute brightness of fireflies

uses the optimization of firefly population to complete the learning of brain EC networks

and uses reproductive mechanism to further optimize the population. First

a firefly individual represented a brain EC network with a few edges

which was gradually constructed through the directional movements and random movements of the firefly individual. Then

a reproductive mechanism was employed to optimize the quality of networks after a certain number of evolution iterations. Finally

the network structure represented by the individuals with the highest absolute brightness in the population was used as the learning brain EC network. Experimental results on many simulated datasets verified the effectiveness of the reproductive mechanism

and the new algorithm has obvious advantages on the whole performance compared with other algorithms. Experimental results on real datasets also show the potential practicability of the new algorithm.