  Non-dominated sorting genetic algorithm II (NSGA-II) does well in dealing
with multi-objective problems. When evaluating validity of an algorithm for
multi-objective problems, two kinds of indices are often considered
simultaneously, i.e. the convergence to Pareto Front and the distribution
characteristic. The crowding distance in the standard NSGA-II has the property
that solutions within a cubic have the same crowding distance, which has no
contribution to the convergence of the algorithm. Actually the closer to the
Pareto Front a solution is, the higher priority it should have. In the paper,
the crowding distance is redefined while keeping almost all the advantages of
the original one. Moreover, the speed of converging to the Pareto Front is
faster. Finally, the improvement is proved to be effective by applying it to
solve nine Benchmark problems.
