With　the　increase　of　the　objective　dimension　of　optimization　problems,　the　effect　of　comparing　individuals　through　Pareto　dominance　relation　drops　sharply.　While　some　algorithms　enhance　Pareto　dominance　via　diversity　preservation　strategies,　performance　indicators,　and　reference　vectors,　many　of　them　encounter　difficulties　in　balancing　the　convergence　and　diversity　of　populations.　Therefore,　this　paper　proposes　a　subspace　segmentation　based　co-evolutionary　algorithm　for　balancing　convergence　and　diversity　in　many-objective　optimization.　First,　the　decision　space　is　divided　into　a　convergence　subspace　and　a　diversity　subspace,　which　are　searched　in　the　early　and　late　stages　to　improve　the　population　convergence　and　diversity,　respectively.　Second,　a　capacity　adaptively　adjusted　archive　is　used　to　retain　elite　individuals　with　better　convergence　in　the　population,　which　is　further　used　to　mate　with　the　population.　Moreover,　an　indicator　with　penalty　factor　is　proposed　to　retain　the　boundary　individuals　so　as　to　maintain　the　population　diversity.　Comparing　with　6　advanced　many-objective　evolutionary　algorithms　on　63　benchmark　cases,　the　proposed　algorithm　obtains　smallest　IGD　on　36　benchmark　cases,　the　experimental　results　show　that　the　proposed　algorithm　can　balance　convergence　and　diversity　well　and　has　exhibit　competitiveness.　©　2023　Elsevier　B.V.