Feature　selection　is　a　critical　preprocessing　technique　used　to　remove　irrelevant　and　redundant　features　from　datasets　while　maintaining　or　improving　the　accuracy　of　machine　learning　models.　Recent　advancements　in　this　area　have　primarily　focused　on　wrapper-based　feature　selection　methods,　which　leverage　metaheuristic　search　algorithms　(MSAs)　to　identify　optimal　feature　subsets.　In　this　paper,　we　propose　a　novel　wrapper-based　feature　selection　method　utilizing　the　Triangulation　Topology　Aggregation　Optimizer　(TTAO),　a　newly　developed　algorithm　inspired　by　the　geometric　properties　of　triangular　topology　and　similarity.　To　adapt　the　TTAO　for　binary　feature　selection　tasks,　we　introduce　a　conversion　mechanism　that　transforms　continuous　decision　variables　into　binary　space,　allowing　the　TTAO-which　is　inherently　designed　for　real-valued　problems-to　function　efficiently　in　binary　domains.　TTAO　incorporates　two　distinct　search　strategies,　generic　aggregation　and　local　aggregation,　to　maintain　an　effective　balance　between　global　exploration　and　local　exploitation.　Through　extensive　experimental　evaluations　on　a　wide　range　of　benchmark　datasets,　TTAO　demonstrates　superior　performance　over　conventional　MSAs　in　feature　selection　tasks.　The　results　highlight　TTAO＇s　capability　to　enhance　model　accuracy　and　computational　efficiency,　positioning　it　as　a　promising　tool　to　advance　feature　selection　and　support　industrial　innovation　in　data-driven　tasks.