Improved Crowding Distance for NSGA-II

Understand

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.