In　this　study,　we　introduce　two　improved　assessment　metrics　of　multiobjective　optimizers,　Nondominated　Ratio　and　Spacing　Distribution,　and　analyze　their　rationality　and　validity.　Based　on　the　concept　of　Immunodominance　and　Antibody　Clonal　Selection　Theory,　a　novel　multiobjective　optimization　algorithm,　Immune　Dominance　Clonal　Multiobjective　Algorithm　(IDCMA),　is　put　forward.　The　simulation　comparisons　between　IDCMA　and　the　Strength　Pareto　Evolutionary　Algorithm　show　that　IDCMA　has　the　best　performance　in　popular　metrics　such　as　Spacing,　Coverage　of　Two　Sets　and　the　two　new　metrics　presented　in　this　paper　when　low-dimensional　multiobjective　problems　are　concerned.　The　statistical　results　of　the　four　metrics　also　show　that　Spacing　Distribution　conquers　some　limitations　of　Spacing　triumphantly,　and　Nondominated　Ratio　conquers　the　limitation　of　Coverage　of　Two　Sets　that　only　compared　between　two　sets.