Meta-heuristic　algorithms　are　popular　for　their　efficiency　in　solving　complex　optimization　problems.　Although　there　are　many　known　algorithms,　identifying　ways　to　improve　their　performance　remains　an　important　research　area.　This　paper　proposes　a　brain　neuroscience-inspired　meta-heuristic　algorithm　called　the　Neural　Population　Dynamics　Optimization　Algorithm　(NPDOA).　There　are　three　strategies　in　NPDOA.　(1)　The　attractor　trending　strategy　drives　neural　populations　towards　optimal　decisions,　thereby　ensuring　exploitation　capability.　(2)　The　coupling　disturbance　strategy　deviates　neural　populations　from　attractors　by　coupling　with　other　neural　populations,　thus　improving　exploration　ability.　(3)　The　information　projection　strategy　controls　the　communication　between　neural　populations,　enabling　a　transition　from　exploration　to　exploitation.　The　results　of　benchmark　and　practical　problems　verified　the　effectiveness　of　NPDOA.　©　2024　Elsevier　B.V.