Harris　hawks　optimization　(HHO)　is　a　recently　developed　meta-heuristic　optimization　algorithm　based　on　hunting　behavior　of　Harris　hawks.　Similar　to　other　meta-heuristic　algorithms,　HHO　tends　to　be　trapped　in　low　diversity,　local　optima　and　unbalanced　exploitation　ability.　In　order　to　improve　the　performance　of　HHO,　a　novel　quasi-reflected　Harris　hawks　algorithm　(QRHHO)　is　proposed,　which　combines　HHO　algorithm　and　quasi-reflection-based　learning　mechanism　(QRBL)　together.　The　improvement　includes　two　parts:　the　QRBL　mechanism　is　introduced　firstly　to　increase　the　population　diversity　in　the　initial　stage,　and　then,　QRBL　is　added　in　each　population　position　update　to　improve　the　convergence　rate.　The　proposed　method　will　also　be　helpful　to　control　the　balance　between　exploration　and　exploitation.　The　performance　of　QRHHO　has　been　tested　on　twenty-three　benchmark　functions　of　various　types　and　dimensions.　Through　comparison　with　the　basic　HHO,　HHO　combined　with　opposition-based　learning　mechanism　and　HHO　combined　with　quasi-opposition-based　learning　mechanism,　the　results　demonstrate　that　QRHHO　can　effectively　improve　the　convergence　speed　and　solution　accuracy　of　the　basic　HHO　and　two　variants　of　HHO.　At　the　same　time,　QRHHO　is　also　better　than　other　swarm-based　intelligent　algorithms.　©　2020,　Springer-Verlag　GmbH　Germany,　part　of　Springer　Nature.