Association　rule　mining　(ARM)　is　one　of　the　core　techniques　of　data　mining　to　discover　potentially　valuable　association　relationships　from　mixed　datasets.　In　the　current　research,　various　heuristic　algorithms　have　been　introduced　into　ARM　to　address　the　high　computation　time　of　traditional　ARM.　Although　a　more　detailed　review　of　the　heuristic　algorithms　based　on　ARM　is　available,　this　paper　differs　from　the　existing　reviews　in　that　we　expected　it　to　provide　a　more　comprehensive　and　multi-faceted　survey　of　emerging　research,　which　could　provide　a　reference　for　researchers　in　the　field　to　help　them　understand　the　state-of-the-art　PSO-based　ARM　algorithms.　In　this　paper,　we　review　the　existing　research　results.　Heuristic　algorithms　for　ARM　were　divided　into　three　main　groups,　including　biologically　inspired,　physically　inspired,　and　other　algorithms.　Additionally,　different　types　of　ARM　and　their　evaluation　metrics　are　described　in　this　paper,　and　the　current　status　of　the　improvement　in　PSO　algorithms　is　discussed　in　stages,　including　swarm　initialization,　algorithm　parameter　optimization,　optimal　particle　update,　and　velocity　and　position　updates.　Furthermore,　we　discuss　the　applications　of　PSO-based　ARM　algorithms　and　propose　further　research　directions　by　exploring　the　existing　problems.