In　this　paper,　a　novel　hybrid　meta-heuristic　algorithm　called　ESSAWOA　is　proposed　for　solving　global　optimization　problems.　The　main　idea　of　ESSAWOA　is　to　enhance　Whale　Optimization　Algorithm　(WOA)　by　combining　the　mechanism　of　Salp　Swarm　Algorithm　(SSA)　and　Lens　Opposition-based　Learning　strategy　(LOBL).　The　hybridization　process　includes　three　parts:　First,　the　leader　mechanism　with　strong　exploitation　of　SSA　is　applied　to　update　the　population　position　before　the　basic　WOA　operation.　Second,　the　nonlinear　parameter　related　to　the　convergence　property　in　SSA　is　introduced　to　the　two　phases　of　encircling　prey　and　bubble-net　attacking　in　WOA.　Third,　LOBL　strategy　is　used　to　increase　the　population　diversity　of　the　proposed　optimizer.　The　hybrid　design　is　expected　to　significantly　enhance　the　exploitation　and　exploration　capacity　of　the　proposed　algorithm.　To　investigate　the　effectiveness　of　ESSAWOA,　twenty-three　benchmark　functions　of　different　dimensions　and　three　classical　engineering　design　problems　are　performed.　Furthermore,　SSA,　WOA　and　seven　other　well-known　meta-heuristic　algorithms　are　employed　to　compare　with　the　proposed　optimizer.　Our　results　reveal　that　ESSAWOA　can　effectively　and　quickly　obtain　the　promising　solution　of　these　optimization　problems　in　the　search　space.　The　performance　of　ESSAWOA　is　significantly　superior　to　the　basic　WOA,　SSA　and　other　meta-heuristic　algorithms.