Feature　selection　is　a　vital　technique　that　enhances　the　quality　of　input　datasets　by　reducing　redundancy,　noise,　and　inaccuracies　without　compromising　classifier　accuracy.　The　integration　of　metaheuristic　search　algorithms　(MSAs)　into　feature　selection　enables　the　discovery　of　relevant　features,　thereby　simplifying　dataset　representation.　However,　despite　the　proliferation　of　MSAs　based　on　diverse　sources　of　inspiration　in　recent　years,　their　application　to　solve　real-world　optimization　problems,　such　as　feature　selection,　remains　largely　unexplored.　This　paper　introduces　a　novel　MSA　called　sperm　swarm　optimization　(SSO),　inspired　by　the　mobility　behavior　of　sperms　during　the　fertilization　process,　to　develop　a　robust　wrapper-based　feature　selection　method.　To　evaluate　the　effectiveness　of　SSO　in　solving　feature　selection　problems,　extensive　simulations　are　conducted　using　ten　datasets　from　the　UCI　Machine　Learning　Repository.　The　results　are　compared　with　five　other　prominent　MSAs.　Extensive　simulations　demonstrate　that　SSO　outperforms　the　peer　algorithms　in　terms　of　mean　accuracy　and　feature　selection　efficiency　across　the　majority　of　the　employed　datasets.　The　experimental　results　provide　compelling　evidence　for　the　efficacy　and　potential　of　SSO　in　addressing　feature　selection　challenges.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.　2024.