A　new　method　to　learn　brain　effective　connectivity　(EC)　networks　by　parallel　searching　of　double　firefly　populations　in　order　to　identify　high-quality　brain　EC　network　from　functional　magnetic　resonance　imaging　(fMRI)　data.　The　double　population　optimization　was　used　to　learn　brain　EC　networks.　The　initial　population　was　divided　into　an　elite　population　and　a　common　population.　Then　the　brain　EC　networks　were　gradually　constructed　through　the　directional　movements　of　the　elite　population　and　the　random　movements　of　the　common　population.　The　population　sizes　of　the　elite　population　and　the　common　population　were　dynamically　adjusted　in　the　process　of　network　constructions,　and　the　information　exchange　between　the　two　populations　was　realized　by　using　a　population　migration　operation.　An　adaptive　updating　mechanism　based　on　a　diversity　measure　was　used　to　dynamically　update　the　two　populations　after　a　certain　number　of　evolution　iterations.　The　experimental　results　on　many　simulated　datasets　verify　that　the　new　algorithm　has　obvious　advantages　on　the　whole　performance　compared　with　other　algorithms,　and　shows　the　potential　practicability　of　the　new　algorithm　on　real　datasets.　©　2020,　Zhejiang　University　Press.　All　right　reserved.