Inverted　generational　distance　is　a　widely　used　indicator　for　evaluating　many-objective　optimisation　algorithms.　In　the　past　several　years,　numerous　researchers　have　paid　much　attention　to　the　improvement　of　many-objective　optimisation　algorithms,　while　few　researchers　have　mathematically　analysed　inverted　generational　distance.　In　this　paper,　we　present　detailed　mathematical　analyses　of　inverted　generational　distance,　and　then　reveal　the　relation　between　generational　distance　and　inverted　generational　distance.　The　conclusion　is　drawn　that　convergence　plays　different　roles　in　different　stages.　Experimental　results　on　seven　many-objective　benchmark　problems　verify　our　analyses.