The　problem　of　convergence　and　diversity　in　the　course　of　population　evolution　is　difficult　to　be　balanced　for　solving　the　many-objective　optimization　problem　(MaOP).　To　track　with　the　problem,　a　many-objective　optimization　algorithm　is　designed.　In　the　algorithm,　a　hybrid　selection　mechanism　under　the　concurrent　integration　strategy　is　built　to　improve　algorithm　performance　by　employing　the　different　selection　operators.　The　concurrent　integration　strategy　can　select　the　suitable　operator　to　balance　the　convergence　and　diversity　of　the　solution　in　the　course　of　the　population　evolutionary.　To　verify　the　effectiveness　of　the　algorithm,　the　designed　algorithm　is　compared　with　other　five　excellent　many-objective　algorithms　on　DTLZ　and　WFG　test　problem.　What　is　more,　the　designed　algorithm　is　applied　to　solve　the　coal　green　production　optimization　problem.　The　simulation　results　show　that　the　performance　of　designed　algorithm　is　superior　to　whether　the　DTLZ　and　WFG　test　problem　or　the　application　problem.