Selection　strategy　is　an　essential　evolutionary　component　for　many-objective　evolutionary　algorithms,　including　mating　selection　and　environmental　selection.　However,　there　are　still　many　challenges　in　evolution　as　the　number　of　objectives　increases,　such　as　the　conflict　between　diversity　and　convergence,　and　insufficient　Pareto　selection　pressure.　To　address　these　problems,　this　paper　proposes　a　coordinated　many-objective　evolutionary　algorithm　using　random　adaptive　parameters　(MaOEA-CO).　Specifically,　the　algorithm　adopts　the　coordinated　selection　mechanism　as　a　new　mating　selection　strategy　that　regulates　the　diversity　and　convergence　weight　of　individuals　through　random　adaptive　parameters　design,　which　can　better　balance　the　diversity　and　convergence　of　individuals　at　the　edge　of　the　Pareto　front.　Moreover,　an　environmental　selection　method　based　on　coordination　angle　and　Pareto　distance　indicators　is　designed.　The　angle　indicator　selects　the　two　less　diverse　individuals　from　the　whole　population,　and　the　Pareto　distance　indicator　is　used　to　remove　the　poor　convergence　individuals.　We　are　ensuring　population　diversity　while　improving　the　selection　pressure　of　the　algorithm.　The　results　of　comparative　experiments　conducted　on　the　standard　test　suite　and　Wilcoxon　demonstrate　the　superiority　of　the　MaOEA-CO　algorithm　in　comparison　with　six　state-of-the-art　designs　in　terms　of　solution　quality　and　computational　efficiency.　Besides,　a　many-objective　coal　model　is　applied　to　verify　the　performance　of　the　MaOEA-CO　algorithm　further.　The　algorithm　provides　a　better　Pareto　solution　and　promotes　the　development　of　coal　enterprises.