Similar　to　other　swarm-based　algorithms,　the　recently　developed　whale　optimization　algorithm　(WOA)　has　the　problems　of　low　accuracy　and　slow　convergence.　It　is　also　easy　to　fall　into　local　optimum.　Moreover,　WOA　and　its　variants　cannot　perform　well　enough　in　solving　high-dimensional　optimization　problems.　This　paper　puts　forward　a　new　improved　WOA　with　joint　search　mechanisms　called　JSWOA　for　solving　the　above　disadvantages.　First,　the　improved　algorithm　uses　tent　chaotic　map　to　maintain　the　diversity　of　the　initial　population　for　global　search.　Second,　a　new　adaptive　inertia　weight　is　given　to　improve　the　convergence　accuracy　and　speed,　together　with　jump　out　from　local　optimum.　Finally,　to　enhance　the　quality　and　diversity　of　the　whale　population,　as　well　as　increase　the　probability　of　obtaining　global　optimal　solution,　opposition-based　learning　mechanism　is　used　to　update　the　individuals　of　the　whale　population　continuously　during　each　iteration　process.　The　performance　of　the　proposed　JSWOA　is　tested　by　twenty-three　benchmark　functions　of　various　types　and　dimensions.　Then,　the　results　are　compared　with　the　basic　WOA,　several　variants　of　WOA　and　other　swarm-based　intelligent　algorithms.　The　experimental　results　show　that　the　proposed　JSWOA　algorithm　with　multi-mechanisms　is　superior　to　WOA　and　the　other　state-of-the-art　algorithms　in　the　competition,　exhibiting　remarkable　advantages　in　the　solution　accuracy　and　convergence　speed.　It　is　also　suitable　for　dealing　with　high-dimensional　global　optimization　problems.