The　Set　Orienteering　Problem　(SOP)　is　a　variant　of　the　popular　Orienteering　Problem　(OP)　arising　from　a　number　of　real-life　applications.　The　aim　is　to　find　a　tour　across　a　subset　of　customers,　while　maximizing　the　collected　profit　within　a　given　travel　time　limit.　In　SOP,　vertices　(customers)　are　partitioned　into　clusters,　where　a　profit　is　associated　to　each　cluster.　The　profit　of　a　cluster　is　collected　only　if　at　least　one　vertex　belonging　to　the　cluster　is　contained　in　the　tour.　For　this　NP-hard　problem,　we　present　a　highly　effective　hybrid　evolutionary　algorithm　that　integrates　a　cluster-based　crossover　operator,　a　randomized　mutation　operator　to　generate　multiple　distinct　promising　offspring　solutions,　and　a　two-phase　local　refinement　procedure　that　explores　both　feasible　and　infeasible　solutions　in　search　of　high-quality　local　optima.　Extensive　experiments　on　192　large　benchmark　instances　show　that　the　proposed　algorithm　significantly　outperforms　all　the　existing　approaches　from　the　SOP　literature.　In　particular,　it　reports　improved　best-known　solutions　(new　lower　bounds)　for　54　instances,　while　matching　the　existing　best-known　result　for　133　instances.　We　further　investigate　the　contribution　of　the　key　algorithmic　elements　to　the　success　of　the　proposed　approach.　©　2023　Elsevier　Inc.